diff --git a/.rosinstall b/.rosinstall
index 43cc7e9b8..17d75d6c7 100644
--- a/.rosinstall
+++ b/.rosinstall
@@ -18,6 +18,11 @@
uri: https://github.com/ignc-research/stable-baselines3
version: master
+- git:
+ local-name: ../forks/marl/stable-baselines3
+ uri: https://github.com/ignc-research/stable-baselines3
+ version: marl
+
- git:
local-name: ../forks/navigation/local_planner/teb
uri: https://github.com/rst-tu-dortmund/teb_local_planner
diff --git a/HPP/model/model.py b/HPP/model/model.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/HPP/record.py b/HPP/record.py
new file mode 100644
index 000000000..193803975
--- /dev/null
+++ b/HPP/record.py
@@ -0,0 +1,16 @@
+from arena_marl.marl_agent.utils.supersuit_utils import MarkovVectorEnv_patched
+
+
+def store_data():
+ """
+ Store data in a csv file
+ """
+
+
+class Env_Recorder(MarkovVectorEnv_patched):
+ """
+ A wrapper for the environment that records the state and action
+ """
+
+ def __init__(self):
+ super(Env_Recorder, self).step(self, actions)
diff --git a/HPP/train.py b/HPP/train.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/arena_bringup/launch/marl_start_flatland.launch b/arena_bringup/launch/marl_start_flatland.launch
new file mode 100644
index 000000000..7dacf4f69
--- /dev/null
+++ b/arena_bringup/launch/marl_start_flatland.launch
@@ -0,0 +1,153 @@
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\ No newline at end of file
diff --git a/arena_bringup/launch/marl_start_training.launch b/arena_bringup/launch/marl_start_training.launch
new file mode 100644
index 000000000..5b27d17e0
--- /dev/null
+++ b/arena_bringup/launch/marl_start_training.launch
@@ -0,0 +1,215 @@
+
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+
\ No newline at end of file
diff --git a/arena_bringup/launch/start_arena_flatland.launch b/arena_bringup/launch/start_arena_flatland.launch
index 2aac3af96..e026cb07b 100755
--- a/arena_bringup/launch/start_arena_flatland.launch
+++ b/arena_bringup/launch/start_arena_flatland.launch
@@ -38,7 +38,7 @@
-
+
diff --git a/arena_bringup/launch/sublaunch_training/marl_fake_localization.launch b/arena_bringup/launch/sublaunch_training/marl_fake_localization.launch
new file mode 100644
index 000000000..e3d5ed46c
--- /dev/null
+++ b/arena_bringup/launch/sublaunch_training/marl_fake_localization.launch
@@ -0,0 +1,88 @@
+
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diff --git a/arena_bringup/launch/sublaunch_training/marl_single_env_training.launch b/arena_bringup/launch/sublaunch_training/marl_single_env_training.launch
new file mode 100644
index 000000000..4a0eb1add
--- /dev/null
+++ b/arena_bringup/launch/sublaunch_training/marl_single_env_training.launch
@@ -0,0 +1,60 @@
+
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\ No newline at end of file
diff --git a/arena_bringup/rviz/nav_LP_2.rviz b/arena_bringup/rviz/nav_LP_2.rviz
new file mode 100644
index 000000000..efb888592
--- /dev/null
+++ b/arena_bringup/rviz/nav_LP_2.rviz
@@ -0,0 +1,1039 @@
+Panels:
+ - Class: rviz/Displays
+ Help Height: 84
+ Name: Displays
+ Property Tree Widget:
+ Expanded:
+ - /Map1
+ - /obstacles1
+ - /Plan Manager1/subgoal1
+ - /Tracking-Visuals1/TrackedPersons1
+ - /Tracking-Visuals1/TrackedPersons1/History as line1
+ - /Tracking-Visuals1/TrackedGroups1
+ - /AIO1/Dynamic Scan1
+ - /Agent path taken1
+ Splitter Ratio: 0.4374079406261444
+ Tree Height: 357
+ - Class: rviz/Selection
+ Name: Selection
+ - Class: rviz/Tool Properties
+ Expanded:
+ - /2D Pose Estimate1
+ - /2D Nav Goal1
+ - /Publish Point1
+ Name: Tool Properties
+ Splitter Ratio: 0.4285709857940674
+ - Class: rviz/Views
+ Expanded:
+ - /Current View1
+ Name: Views
+ Splitter Ratio: 0.5
+ - Class: rviz/Time
+ Experimental: false
+ Name: Time
+ SyncMode: 0
+ SyncSource: LaserScan
+Preferences:
+ PromptSaveOnExit: false
+Toolbars:
+ toolButtonStyle: 2
+Visualization Manager:
+ Class: ""
+ Displays:
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /visualizer/path
+ Name: Path_Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/TF
+ Enabled: true
+ Frame Timeout: 15
+ Frames:
+ All Enabled: false
+ base_link:
+ Value: true
+ chassis_link:
+ Value: true
+ front_fender_link:
+ Value: true
+ front_laser:
+ Value: true
+ front_laser_mount:
+ Value: true
+ front_left_wheel_link:
+ Value: true
+ front_mount:
+ Value: true
+ front_right_wheel_link:
+ Value: true
+ imu_link:
+ Value: true
+ map:
+ Value: true
+ mid_mount:
+ Value: true
+ navsat_link:
+ Value: true
+ odom:
+ Value: true
+ rear_fender_link:
+ Value: true
+ rear_left_wheel_link:
+ Value: true
+ rear_mount:
+ Value: true
+ rear_right_wheel_link:
+ Value: true
+ Marker Alpha: 1
+ Marker Scale: 1
+ Name: TF
+ Show Arrows: true
+ Show Axes: true
+ Show Names: true
+ Tree:
+ map:
+ odom:
+ base_link:
+ chassis_link:
+ front_fender_link:
+ {}
+ front_left_wheel_link:
+ {}
+ front_right_wheel_link:
+ {}
+ imu_link:
+ {}
+ mid_mount:
+ front_mount:
+ front_laser_mount:
+ front_laser:
+ {}
+ rear_mount:
+ {}
+ navsat_link:
+ {}
+ rear_fender_link:
+ {}
+ rear_left_wheel_link:
+ {}
+ rear_right_wheel_link:
+ {}
+ Update Interval: 0
+ Value: true
+ - Alpha: 1
+ Class: rviz/RobotModel
+ Collision Enabled: false
+ Enabled: true
+ Links:
+ All Links Enabled: true
+ Expand Joint Details: false
+ Expand Link Details: false
+ Expand Tree: false
+ Link Tree Style: Links in Alphabetic Order
+ base_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ chassis_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ front_fender_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ front_laser:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ front_laser_mount:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ front_left_wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ front_mount:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ front_right_wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ imu_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ mid_mount:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ navsat_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ rear_fender_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ rear_left_wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ rear_mount:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ rear_right_wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ Name: RobotModel
+ Robot Description: robot_description
+ TF Prefix: ""
+ Update Interval: 0
+ Value: true
+ Visual Enabled: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: false
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /move_base_simple/goal
+ Unreliable: false
+ Value: false
+ Enabled: true
+ Name: robot
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /cadrl/goal_path_marker
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /cadrl/other_agents_markers
+ Name: MarkerArray
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ Enabled: false
+ Name: cadrl
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 0.699999988079071
+ Class: rviz/Map
+ Color Scheme: map
+ Draw Behind: false
+ Enabled: true
+ Name: Map
+ Topic: /map
+ Unreliable: false
+ Use Timestamp: false
+ Value: true
+ - Alpha: 0.699999988079071
+ Class: rviz/Map
+ Color Scheme: map
+ Draw Behind: false
+ Enabled: false
+ Name: Global_costmap
+ Topic: /move_base/global_costmap/costmap
+ Unreliable: false
+ Use Timestamp: false
+ Value: false
+ - Alpha: 0.699999988079071
+ Class: rviz/Map
+ Color Scheme: costmap
+ Draw Behind: false
+ Enabled: false
+ Name: Local_costmap
+ Topic: /move_base/local_costmap/costmap
+ Unreliable: false
+ Use Timestamp: false
+ Value: false
+ Enabled: true
+ Name: Map
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 255; 0; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: teb
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 0; 0
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/TebLocalPlannerROS/local_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 85; 0; 127
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: mpc
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/MpcLocalPlannerROS/local_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 0; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.029999999329447746
+ Name: dwa
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/DWAPlannerROS/local_plan
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: Local plan
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 170; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: teb
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 0; 0
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/TebLocalPlannerROS/global_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 170; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: mpc
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/MpcLocalPlannerROS/global_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 170; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.029999999329447746
+ Name: dwa
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/DWAPlannerROS/global_plan
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: Global Plan
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 0.30399999022483826
+ Min Value: 0.30399999022483826
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/LaserScan
+ Color: 255; 0; 0
+ Color Transformer: FlatColor
+ Decay Time: 0
+ Enabled: true
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: LaserScan
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 8
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /scan
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: true
+ Enabled: true
+ Name: Sensors
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /action
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Group
+ Displays: ~
+ Enabled: true
+ Name: obstacles
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /cadrl/goal_path_marker
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /other_agents_markers
+ Name: MarkerArray
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ Enabled: true
+ Name: crowdnav
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 25; 255; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Lines
+ Line Width: 0.029999999329447746
+ Name: global_path
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /vis_global_path
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 85; 170; 255
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.30000001192092896
+ Name: local_traj
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /vis_path_timed_astar
+ Unreliable: false
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /vis_goal
+ Name: goal
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /planning_vis/subgoal
+ Name: subgoal
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /vis_landmarks_
+ Name: landmarks
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /vis_triangle
+ Name: triangulation_map
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: occmap_local_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/occupancy
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: occmap_dynamic_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/occupancy_dynamic
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: occmap_static_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/occupancy_static
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: esdfmap_local_fused
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/esdf
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: esdfmap_static_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/esdf_static
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ Enabled: true
+ Name: Plan Manager
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 255; 25; 0
+ Enabled: false
+ Head Length: 0.30000001192092896
+ Head Radius: 0.10000000149011612
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 1
+ Shaft Radius: 0.05000000074505806
+ Shape: Axes
+ Topic: /goal
+ Unreliable: false
+ Value: false
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /time_space/vis_visted_samples
+ Name: samples
+ Namespaces:
+ {}
+ Queue Size: 1
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /planning_vis/goal
+ Name: Marker
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 25; 255; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Lines
+ Line Width: 0.029999999329447746
+ Name: Path
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /globalPlan
+ Unreliable: false
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /planning_vis/subgoal
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 255; 25; 0
+ Enabled: true
+ Head Length: 0.30000001192092896
+ Head Radius: 0.10000000149011612
+ Name: Pose
+ Queue Size: 10
+ Shaft Length: 1
+ Shaft Radius: 0.05000000074505806
+ Shape: Arrow
+ Topic: /subgoal
+ Unreliable: false
+ Value: true
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Class: spencer_tracking_rviz_plugin/TrackedPersons
+ Color: 130; 130; 130
+ Color map offset: 0
+ Color transform: SRL Tracking Colors
+ Delete after no. cycles: 5
+ Enabled: true
+ Excluded person IDs: ""
+ Font color: 255; 255; 255
+ Font color style: Same color
+ Font scale: 2
+ History as line:
+ Line width: 0.05000000074505806
+ Value: true
+ History size: 10
+ Included person IDs: ""
+ Min. history point distance: 0.4000000059604645
+ Missed alpha: 1
+ Name: TrackedPersons
+ Occlusion alpha: 1
+ Queue Size: 10
+ Render covariances:
+ Line width: 0.10000000149011612
+ Value: false
+ Render detection IDs: false
+ Render history: false
+ Render person visual: true
+ Render track IDs: true
+ Render track state: false
+ Render velocities: true
+ Show DELETED tracks: false
+ Show MATCHED tracks: true
+ Show MISSED tracks: true
+ Show OCCLUDED tracks: true
+ Style:
+ Line width: 0.05000000074505806
+ Scaling factor: 1
+ Value: Bounding boxes
+ Topic: /pedsim_visualizer/tracked_persons
+ Unreliable: false
+ Value: true
+ Z offset:
+ Use Z position from message: false
+ Value: 0
+ - Alpha: 0.699999988079071
+ Class: spencer_tracking_rviz_plugin/TrackedGroups
+ Color: 130; 130; 130
+ Color map offset: 0
+ Color transform: Flat
+ Connect group members: true
+ Enabled: true
+ Excluded group IDs: ""
+ Excluded person IDs: ""
+ Font color: 255; 255; 255
+ Font color style: Same color
+ Font scale: 2
+ Global history size: 5
+ Group ID Z offset: 2
+ Included group IDs: ""
+ Included person IDs: ""
+ Name: TrackedGroups
+ Occlusion alpha: 1
+ Queue Size: 10
+ Render group IDs:
+ Hide IDs of single-person groups: false
+ Value: true
+ Render history: false
+ Single-person groups in constant color: false
+ Style:
+ Line width: 0.05000000074505806
+ Scaling factor: 1
+ Value: Simple
+ Topic: /pedsim_visualizer/tracked_groups
+ Tracked persons topic: /pedsim_visualizer/tracked_persons
+ Unreliable: false
+ Value: true
+ Z offset:
+ Use Z position from message: false
+ Value: 0
+ Enabled: true
+ Name: Tracking-Visuals
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/LaserScan
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: true
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: LaserScan
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.03999999910593033
+ Style: Flat Squares
+ Topic: /scan_new
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: true
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /all_in_one_planner/all_in_one_action_trajectory_vis
+ Name: Planner Selection over Path
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /all_in_one_planner/all_in_one_action_vis
+ Name: Planner Selection
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/LaserScan
+ Color: 252; 233; 79
+ Color Transformer: FlatColor
+ Decay Time: 0
+ Enabled: true
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: Dynamic Scan
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.3499999940395355
+ Style: Flat Squares
+ Topic: /all_in_one_planner/scan_dynamic
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /all_in_one_planner/vis_subgoal
+ Name: Subgoal
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ Enabled: true
+ Name: AIO
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /viz_agent_pose
+ Name: Agent path taken
+ Namespaces:
+ agent: true
+ Queue Size: 100
+ Value: true
+ Enabled: true
+ Global Options:
+ Background Color: 0; 0; 0
+ Default Light: true
+ Fixed Frame: map
+ Frame Rate: 30
+ Name: root
+ Tools:
+ - Class: rviz/MoveCamera
+ - Class: rviz/Interact
+ Hide Inactive Objects: true
+ - Class: rviz/Select
+ - Class: rviz/SetInitialPose
+ Theta std deviation: 0.2617993950843811
+ Topic: /initialpose
+ X std deviation: 0.5
+ Y std deviation: 0.5
+ - Class: rviz/SetGoal
+ Topic: /move_base_simple/goal
+ - Class: rviz_plugins/Goal3DTool
+ Topic: manual_goal
+ - Class: rviz/PublishPoint
+ Single click: true
+ Topic: /clicked_point
+ Value: true
+ Views:
+ Current:
+ Angle: -7.859952449798584
+ Class: rviz/TopDownOrtho
+ Enable Stereo Rendering:
+ Stereo Eye Separation: 0.05999999865889549
+ Stereo Focal Distance: 1
+ Swap Stereo Eyes: false
+ Value: false
+ Invert Z Axis: false
+ Name: Current View
+ Near Clip Distance: 0.009999999776482582
+ Scale: 40.95969772338867
+ Target Frame:
+ X: 0.17317672073841095
+ Y: -2.2967824935913086
+ Saved: ~
+Window Geometry:
+ Displays:
+ collapsed: true
+ Height: 1367
+ Hide Left Dock: true
+ Hide Right Dock: false
+ QMainWindow State: 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
+ Selection:
+ collapsed: false
+ Time:
+ collapsed: false
+ Tool Properties:
+ collapsed: false
+ Views:
+ collapsed: false
+ Width: 2488
+ X: 72
+ Y: 27
\ No newline at end of file
diff --git a/arena_bringup/rviz/nav_MARL.rviz b/arena_bringup/rviz/nav_MARL.rviz
new file mode 100644
index 000000000..82f8bb367
--- /dev/null
+++ b/arena_bringup/rviz/nav_MARL.rviz
@@ -0,0 +1,1602 @@
+Panels:
+ - Class: rviz/Displays
+ Help Height: 78
+ Name: Displays
+ Property Tree Widget:
+ Expanded:
+ - /Sensors1
+ - /obstacles1
+ - /obstacles1/dynamic1/01
+ - /obstacles1/dynamic1/11
+ - /obstacles1/dynamic1/21
+ - /obstacles1/dynamic1/31
+ - /obstacles1/dynamic1/41
+ - /obstacles1/dynamic1/51
+ - /obstacles1/dynamic1/61
+ - /obstacles1/dynamic1/71
+ - /obstacles1/dynamic1/81
+ - /obstacles1/dynamic1/91
+ - /crowdnav1/Marker1
+ - /Plan Manager1/goal1
+ - /Plan Manager1/subgoal1
+ - /MARL1
+ - /MARL1/robot_11
+ - /MARL1/robot_21/my_robot1
+ - /MARL1/robot_21/Goal Pose1
+ - /MARL1/robot_31
+ - /MARL1/robot_31/my_robot1
+ - /MARL1/robot_31/Goal Pose1
+ - /MARL1/robot_41
+ - /MARL1/robot_51
+ - /MARL1/robot_61/my_robot1
+ - /MARL1/robot_61/Goal Pose1
+ - /MARL1/robot_71/my_robot1
+ - /MARL1/robot_71/Goal Pose1
+ - /MARL1/robot_81/my_robot1
+ - /MARL1/robot_81/Goal Pose1
+ Splitter Ratio: 0.4374079406261444
+ Tree Height: 814
+ - Class: rviz/Selection
+ Name: Selection
+ - Class: rviz/Tool Properties
+ Expanded:
+ - /2D Pose Estimate1
+ - /2D Nav Goal1
+ - /Publish Point1
+ Name: Tool Properties
+ Splitter Ratio: 0.4285709857940674
+ - Class: rviz/Views
+ Expanded:
+ - /Current View1
+ Name: Views
+ Splitter Ratio: 0.5
+ - Class: rviz/Time
+ Experimental: false
+ Name: Time
+ SyncMode: 0
+ SyncSource: ""
+Preferences:
+ PromptSaveOnExit: true
+Toolbars:
+ toolButtonStyle: 2
+Visualization Manager:
+ Class: ""
+ Displays:
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /visualizer/path
+ Name: Path_Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/layer/static
+ Name: static_layer
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 0.699999988079071
+ Class: rviz/Map
+ Color Scheme: map
+ Draw Behind: false
+ Enabled: true
+ Name: Map
+ Topic: /map
+ Unreliable: false
+ Use Timestamp: false
+ Value: true
+ - Alpha: 0.699999988079071
+ Class: rviz/Map
+ Color Scheme: map
+ Draw Behind: false
+ Enabled: false
+ Name: Global_costmap
+ Topic: /move_base/global_costmap/costmap
+ Unreliable: false
+ Use Timestamp: false
+ Value: false
+ - Alpha: 0.699999988079071
+ Class: rviz/Map
+ Color Scheme: costmap
+ Draw Behind: false
+ Enabled: false
+ Name: Local_costmap
+ Topic: /move_base/local_costmap/costmap
+ Unreliable: false
+ Use Timestamp: false
+ Value: false
+ Enabled: true
+ Name: Map
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /cadrl/goal_path_marker
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /cadrl/other_agents_markers
+ Name: MarkerArray
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ Enabled: false
+ Name: cadrl
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 255; 0; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: teb
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 0; 0
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/TebLocalPlannerROS/local_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 85; 0; 127
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: mpc
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/MpcLocalPlannerROS/local_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 0; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.029999999329447746
+ Name: dwa
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/DWAPlannerROS/local_plan
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: Local plan
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 170; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: teb
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 0; 0
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/TebLocalPlannerROS/global_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 170; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.05000000074505806
+ Name: mpc
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/MpcLocalPlannerROS/global_plan
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 0; 170; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.029999999329447746
+ Name: dwa
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /move_base/DWAPlannerROS/global_plan
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: Global Plan
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 0.30399999022483826
+ Min Value: 0.30399999022483826
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/LaserScan
+ Color: 255; 0; 0
+ Color Transformer: FlatColor
+ Decay Time: 0
+ Enabled: true
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: LaserScan
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 8
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /scan
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: true
+ Enabled: true
+ Name: Sensors
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /action
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_00
+ Name: 0
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_01
+ Name: 1
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_02
+ Name: 2
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_03
+ Name: 3
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_04
+ Name: 4
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_05
+ Name: 5
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_06
+ Name: 6
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_07
+ Name: 7
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_08
+ Name: 8
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_09
+ Name: 9
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_10
+ Name: 10
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_11
+ Name: 11
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_12
+ Name: 12
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_13
+ Name: 13
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_14
+ Name: 14
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_15
+ Name: 15
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_16
+ Name: 16
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_17
+ Name: 17
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_18
+ Name: 18
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_19
+ Name: 19
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_20
+ Name: 20
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_21
+ Name: 21
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_22
+ Name: 22
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_23
+ Name: 23
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_circle_static_24
+ Name: 24
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ Enabled: true
+ Name: static
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_00
+ Name: 0
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_01
+ Name: 1
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_02
+ Name: 2
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_03
+ Name: 3
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_05
+ Name: 4
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_06
+ Name: 5
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_07
+ Name: 6
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_08
+ Name: 7
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_09
+ Name: 8
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_04
+ Name: 9
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_10
+ Name: 10
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_11
+ Name: 11
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_12
+ Name: 12
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_13
+ Name: 13
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_15
+ Name: 14
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_05
+ Name: 15
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_16
+ Name: 16
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_17
+ Name: 17
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_18
+ Name: 18
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_19
+ Name: 19
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_20
+ Name: 20
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_21
+ Name: 21
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_22
+ Name: 22
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_23
+ Name: 23
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_24
+ Name: 24
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_25
+ Name: 25
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_26
+ Name: 26
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_27
+ Name: 27
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_28
+ Name: 28
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_29
+ Name: 29
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_30
+ Name: 30
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_31
+ Name: 31
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_32
+ Name: 32
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_33
+ Name: 33
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_34
+ Name: 34
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_35
+ Name: 35
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_36
+ Name: 36
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_37
+ Name: 37
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_38
+ Name: 38
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /flatland_server/debug/model/obstacle_dynamic_with_traj_39
+ Name: 39
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ Enabled: true
+ Name: dynamic
+ Enabled: true
+ Name: obstacles
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /cadrl/goal_path_marker
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /other_agents_markers
+ Name: MarkerArray
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ Enabled: true
+ Name: crowdnav
+ - Class: rviz/Group
+ Displays:
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 25; 255; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Lines
+ Line Width: 0.029999999329447746
+ Name: global_path
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /vis_global_path
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 85; 170; 255
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Billboards
+ Line Width: 0.30000001192092896
+ Name: local_traj
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /vis_path_timed_astar
+ Unreliable: false
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /vis_goal
+ Name: goal
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /planning_vis/subgoal
+ Name: subgoal
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /vis_landmarks_
+ Name: landmarks
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /vis_triangle
+ Name: triangulation_map
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: occmap_local_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/occupancy
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: occmap_dynamic_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/occupancy_dynamic
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: occmap_static_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/occupancy_static
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: esdfmap_local_fused
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/esdf
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/PointCloud2
+ Color: 255; 255; 255
+ Color Transformer: Intensity
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 255; 255
+ Min Color: 0; 0; 0
+ Name: esdfmap_static_only
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.009999999776482582
+ Style: Points
+ Topic: /sdf_map/esdf_static
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ Enabled: true
+ Name: Plan Manager
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /time_space/vis_visted_samples
+ Name: samples
+ Namespaces:
+ {}
+ Queue Size: 1
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /planning_vis/goal
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Buffer Length: 1
+ Class: rviz/Path
+ Color: 25; 255; 0
+ Enabled: true
+ Head Diameter: 0.30000001192092896
+ Head Length: 0.20000000298023224
+ Length: 0.30000001192092896
+ Line Style: Lines
+ Line Width: 0.029999999329447746
+ Name: Path
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Pose Color: 255; 85; 255
+ Pose Style: None
+ Queue Size: 10
+ Radius: 0.029999999329447746
+ Shaft Diameter: 0.10000000149011612
+ Shaft Length: 0.10000000149011612
+ Topic: /globalPlan
+ Unreliable: false
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /planning_vis/goal
+ Name: Marker
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 117; 80; 123
+ Enabled: true
+ Head Length: 0.5
+ Head Radius: 0.10000000149011612
+ Name: Pose
+ Queue Size: 10
+ Shaft Length: 3
+ Shaft Radius: 0.05000000074505806
+ Shape: Arrow
+ Topic: /sim_1/goal
+ Unreliable: false
+ Value: true
+ - Class: rviz/TF
+ Enabled: true
+ Frame Timeout: 15
+ Frames:
+ All Enabled: false
+ eval_sim_robot1_base_footprint:
+ Value: true
+ eval_sim_robot1_laser_link:
+ Value: true
+ eval_sim_robot1_odom:
+ Value: true
+ eval_sim_robot2_base_footprint:
+ Value: true
+ eval_sim_robot2_laser_link:
+ Value: true
+ eval_sim_robot2_odom:
+ Value: true
+ eval_sim_robot3_base_footprint:
+ Value: true
+ eval_sim_robot3_laser_link:
+ Value: true
+ eval_sim_robot3_odom:
+ Value: true
+ eval_sim_robot4_odom:
+ Value: true
+ eval_sim_robot5_odom:
+ Value: true
+ eval_sim_robot6_odom:
+ Value: true
+ eval_sim_robot7_odom:
+ Value: true
+ eval_sim_robot8_odom:
+ Value: true
+ map:
+ Value: true
+ sim_1_robot1_base_footprint:
+ Value: true
+ sim_1_robot1_laser_link:
+ Value: true
+ sim_1_robot1_odom:
+ Value: true
+ sim_1_robot2_base_footprint:
+ Value: true
+ sim_1_robot2_laser_link:
+ Value: true
+ sim_1_robot2_odom:
+ Value: true
+ sim_1_robot3_base_footprint:
+ Value: true
+ sim_1_robot3_laser_link:
+ Value: true
+ sim_1_robot3_odom:
+ Value: true
+ sim_1_robot4_odom:
+ Value: true
+ sim_1_robot5_odom:
+ Value: true
+ sim_1_robot6_odom:
+ Value: true
+ sim_1_robot7_odom:
+ Value: true
+ sim_1_robot8_odom:
+ Value: true
+ Marker Alpha: 1
+ Marker Scale: 1
+ Name: TF
+ Show Arrows: true
+ Show Axes: true
+ Show Names: true
+ Tree:
+ map:
+ eval_sim_robot1_odom:
+ eval_sim_robot1_base_footprint:
+ eval_sim_robot1_laser_link:
+ {}
+ eval_sim_robot2_odom:
+ eval_sim_robot2_base_footprint:
+ eval_sim_robot2_laser_link:
+ {}
+ eval_sim_robot3_odom:
+ eval_sim_robot3_base_footprint:
+ eval_sim_robot3_laser_link:
+ {}
+ eval_sim_robot4_odom:
+ {}
+ eval_sim_robot5_odom:
+ {}
+ eval_sim_robot6_odom:
+ {}
+ eval_sim_robot7_odom:
+ {}
+ eval_sim_robot8_odom:
+ {}
+ sim_1_robot1_odom:
+ sim_1_robot1_base_footprint:
+ sim_1_robot1_laser_link:
+ {}
+ sim_1_robot2_odom:
+ sim_1_robot2_base_footprint:
+ sim_1_robot2_laser_link:
+ {}
+ sim_1_robot3_odom:
+ sim_1_robot3_base_footprint:
+ sim_1_robot3_laser_link:
+ {}
+ sim_1_robot4_odom:
+ {}
+ sim_1_robot5_odom:
+ {}
+ sim_1_robot6_odom:
+ {}
+ sim_1_robot7_odom:
+ {}
+ sim_1_robot8_odom:
+ {}
+ Update Interval: 0
+ Value: true
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /eval_sim/flatland_server/debug/model/robot1
+ Name: my_robot
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /eval_sim/robot1/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_1
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /eval_sim/flatland_server/debug/model/robot2
+ Name: my_robot
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /eval_sim/robot2/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_2
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /eval_sim/flatland_server/debug/model/robot3
+ Name: my_robot
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /eval_sim/robot3/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_3
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/robot4
+ Name: my_robot
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /sim_1/robot4/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_4
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/robot5
+ Name: my_robot
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /sim_1/robot6/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_5
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/obstacle__random_dynamic_06
+ Name: my_robot
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /sim_1/robot6/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_6
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/robot7
+ Name: my_robot
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /sim_1/robot7/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_7
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/robot8
+ Name: my_robot
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /sim_1/robot8/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_8
+ - Class: rviz/Group
+ Displays:
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /sim_1/flatland_server/debug/model/robot4
+ Name: my_robot
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 255; 0
+ Enabled: true
+ Head Length: 0.10000000149011612
+ Head Radius: 0.15000000596046448
+ Name: Goal Pose
+ Queue Size: 10
+ Shaft Length: 0.5
+ Shaft Radius: 0.029999999329447746
+ Shape: Arrow
+ Topic: /sim_1/robot4/goal
+ Unreliable: false
+ Value: true
+ Enabled: true
+ Name: robot_9
+ Enabled: true
+ Name: MARL
+ Enabled: true
+ Global Options:
+ Background Color: 0; 0; 0
+ Default Light: true
+ Fixed Frame: map
+ Frame Rate: 30
+ Name: root
+ Tools:
+ - Class: rviz/MoveCamera
+ - Class: rviz/Interact
+ Hide Inactive Objects: true
+ - Class: rviz/Select
+ - Class: rviz/SetInitialPose
+ Theta std deviation: 0.2617993950843811
+ Topic: /initialpose
+ X std deviation: 0.5
+ Y std deviation: 0.5
+ - Class: rviz/SetGoal
+ Topic: /move_base_simple/goal
+ - Class: flatland_viz/SpawnModel
+ - Class: rviz_plugins/Goal3DTool
+ Topic: goal
+ - Class: flatland_viz/PauseSim
+ - Class: rviz_plugins/TaskGenerator
+ - Class: rviz/PublishPoint
+ Single click: true
+ Topic: /clicked_point
+ Value: true
+ Views:
+ Current:
+ Angle: -1.569939136505127
+ Class: rviz/TopDownOrtho
+ Enable Stereo Rendering:
+ Stereo Eye Separation: 0.05999999865889549
+ Stereo Focal Distance: 1
+ Swap Stereo Eyes: false
+ Value: false
+ Invert Z Axis: false
+ Name: Current View
+ Near Clip Distance: 0.009999999776482582
+ Scale: 38.79127883911133
+ Target Frame:
+ X: 2.8734543323516846
+ Y: -0.42915862798690796
+ Saved: ~
+Window Geometry:
+ Displays:
+ collapsed: false
+ Height: 1043
+ Hide Left Dock: false
+ Hide Right Dock: false
+ QMainWindow State: 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
+ Selection:
+ collapsed: false
+ Time:
+ collapsed: false
+ Tool Properties:
+ collapsed: false
+ Views:
+ collapsed: false
+ Width: 1920
+ X: 0
+ Y: 0
\ No newline at end of file
diff --git a/arena_bringup/rviz/nav_jackal.rviz b/arena_bringup/rviz/nav_jackal.rviz
index 29ef5766a..aebb5f98c 100755
--- a/arena_bringup/rviz/nav_jackal.rviz
+++ b/arena_bringup/rviz/nav_jackal.rviz
@@ -5,14 +5,13 @@ Panels:
Property Tree Widget:
Expanded:
- /robot1
- - /robot1/my_robot1
- /robot1/Goal Pose1
- /Global Plan1/teb1
- /Sensors1/LaserScan1
- /obstacles1
- /obstacles1/dynamic1/301
Splitter Ratio: 0.4374079406261444
- Tree Height: 787
+ Tree Height: 475
- Class: rviz/Selection
Name: Selection
- Class: rviz/Tool Properties
@@ -1252,18 +1251,18 @@ Visualization Manager:
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
- Scale: 46.09282302856445
+ Scale: 25.905818939208984
Target Frame:
- X: 3.297379970550537
- Y: 2.1553478240966797
+ X: 11.80865478515625
+ Y: 8.243291854858398
Saved: ~
Window Geometry:
Displays:
collapsed: false
- Height: 1016
+ Height: 704
Hide Left Dock: false
Hide Right Dock: false
- QMainWindow State: 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
+ QMainWindow State: 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
Selection:
collapsed: false
Time:
@@ -1272,6 +1271,6 @@ Window Geometry:
collapsed: false
Views:
collapsed: false
- Width: 1848
+ Width: 1294
X: 72
Y: 27
diff --git a/arena_marl/CMakeLists.txt b/arena_marl/CMakeLists.txt
new file mode 100755
index 000000000..7fd10c43d
--- /dev/null
+++ b/arena_marl/CMakeLists.txt
@@ -0,0 +1,213 @@
+cmake_minimum_required(VERSION 3.0.2)
+project(arena_marl)
+
+## Compile as C++11, supported in ROS Kinetic and newer
+# add_compile_options(-std=c++11)
+
+## Find catkin macros and libraries
+## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
+## is used, also find other catkin packages
+find_package(catkin REQUIRED COMPONENTS
+ geometry_msgs
+ nav_msgs
+ roscpp
+ rospy
+ sensor_msgs
+ tf2
+ tf2_geometry_msgs
+ tf2_ros
+ flatland_msgs
+)
+
+## System dependencies are found with CMake's conventions
+# find_package(Boost REQUIRED COMPONENTS system)
+
+
+## Uncomment this if the package has a setup.py. This macro ensures
+## modules and global scripts declared therein get installed
+## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
+catkin_python_setup()
+
+################################################
+## Declare ROS messages, services and actions ##
+################################################
+
+## To declare and build messages, services or actions from within this
+## package, follow these steps:
+## * Let MSG_DEP_SET be the set of packages whose message types you use in
+## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
+## * In the file package.xml:
+## * add a build_depend tag for "message_generation"
+## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET
+## * If MSG_DEP_SET isn't empty the following dependency has been pulled in
+## but can be declared for certainty nonetheless:
+## * add a exec_depend tag for "message_runtime"
+## * In this file (CMakeLists.txt):
+## * add "message_generation" and every package in MSG_DEP_SET to
+## find_package(catkin REQUIRED COMPONENTS ...)
+## * add "message_runtime" and every package in MSG_DEP_SET to
+## catkin_package(CATKIN_DEPENDS ...)
+## * uncomment the add_*_files sections below as needed
+## and list every .msg/.srv/.action file to be processed
+## * uncomment the generate_messages entry below
+## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)
+
+## Generate messages in the 'msg' folder
+# add_message_files(
+# FILES
+# Message1.msg
+# Message2.msg
+# )
+
+## Generate services in the 'srv' folder
+# add_service_files(
+# FILES
+# Service1.srv
+# Service2.srv
+# )
+
+## Generate actions in the 'action' folder
+# add_action_files(
+# FILES
+# Action1.action
+# Action2.action
+# )
+
+## Generate added messages and services with any dependencies listed here
+# generate_messages(
+# DEPENDENCIES
+# geometry_msgs# nav_msgs# sensor_msgs# tf2_geometry_msgs
+# )
+
+################################################
+## Declare ROS dynamic reconfigure parameters ##
+################################################
+
+## To declare and build dynamic reconfigure parameters within this
+## package, follow these steps:
+## * In the file package.xml:
+## * add a build_depend and a exec_depend tag for "dynamic_reconfigure"
+## * In this file (CMakeLists.txt):
+## * add "dynamic_reconfigure" to
+## find_package(catkin REQUIRED COMPONENTS ...)
+## * uncomment the "generate_dynamic_reconfigure_options" section below
+## and list every .cfg file to be processed
+
+## Generate dynamic reconfigure parameters in the 'cfg' folder
+# generate_dynamic_reconfigure_options(
+# cfg/DynReconf1.cfg
+# cfg/DynReconf2.cfg
+# )
+
+###################################
+## catkin specific configuration ##
+###################################
+## The catkin_package macro generates cmake config files for your package
+## Declare things to be passed to dependent projects
+## INCLUDE_DIRS: uncomment this if your package contains header files
+## LIBRARIES: libraries you create in this project that dependent projects also need
+## CATKIN_DEPENDS: catkin_packages dependent projects also need
+## DEPENDS: system dependencies of this project that dependent projects also need
+catkin_package(
+# INCLUDE_DIRS include
+# LIBRARIES arena_local_planner_drl
+# CATKIN_DEPENDS geometry_msgs nav_msgs roscpp rospy sensor_msgs tf2 tf2_geometry_msgs tf2_ros
+# DEPENDS system_lib
+)
+
+###########
+## Build ##
+###########
+
+## Specify additional locations of header files
+## Your package locations should be listed before other locations
+include_directories(
+# include
+ scripts
+ ${catkin_INCLUDE_DIRS}
+)
+
+## Declare a C++ library
+# add_library(${PROJECT_NAME}
+# src/${PROJECT_NAME}/arena_local_planner_drl.cpp
+# )
+
+## Add cmake target dependencies of the library
+## as an example, code may need to be generated before libraries
+## either from message generation or dynamic reconfigure
+# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
+
+## Declare a C++ executable
+## With catkin_make all packages are built within a single CMake context
+## The recommended prefix ensures that target names across packages don't collide
+# add_executable(${PROJECT_NAME}_node src/arena_local_planner_drl_node.cpp)
+
+## Rename C++ executable without prefix
+## The above recommended prefix causes long target names, the following renames the
+## target back to the shorter version for ease of user use
+## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node"
+# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "")
+
+## Add cmake target dependencies of the executable
+## same as for the library above
+# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
+
+## Specify libraries to link a library or executable target against
+# target_link_libraries(${PROJECT_NAME}_node
+# ${catkin_LIBRARIES}
+# )
+
+#############
+## Install ##
+#############
+
+# all install targets should use catkin DESTINATION variables
+# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html
+
+## Mark executable scripts (Python etc.) for installation
+## in contrast to setup.py, you can choose the destination
+catkin_install_python(PROGRAMS
+# scripts/env/flatland_gym_env.py
+ DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
+)
+
+## Mark executables for installation
+## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html
+# install(TARGETS ${PROJECT_NAME}_node
+# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
+# )
+
+## Mark libraries for installation
+## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html
+# install(TARGETS ${PROJECT_NAME}
+# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
+# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
+# RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION}
+# )
+
+## Mark cpp header files for installation
+# install(DIRECTORY include/${PROJECT_NAME}/
+# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
+# FILES_MATCHING PATTERN "*.h"
+# PATTERN ".svn" EXCLUDE
+# )
+
+## Mark other files for installation (e.g. launch and bag files, etc.)
+# install(FILES
+# # myfile1
+# # myfile2
+# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
+# )
+
+#############
+## Testing ##
+#############
+
+## Add gtest based cpp test target and link libraries
+# catkin_add_gtest(${PROJECT_NAME}-test test/test_arena_local_planner_drl.cpp)
+# if(TARGET ${PROJECT_NAME}-test)
+# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
+# endif()
+
+## Add folders to be run by python nosetests
+# catkin_add_nosetests(test)
diff --git a/arena_marl/marl_agent/__init__.py b/arena_marl/marl_agent/__init__.py
new file mode 100755
index 000000000..e69de29bb
diff --git a/arena_marl/marl_agent/envs/__init__.py b/arena_marl/marl_agent/envs/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/arena_marl/marl_agent/envs/pettingzoo_env.py b/arena_marl/marl_agent/envs/pettingzoo_env.py
new file mode 100644
index 000000000..572365e4a
--- /dev/null
+++ b/arena_marl/marl_agent/envs/pettingzoo_env.py
@@ -0,0 +1,390 @@
+"""PettingZoo Environment for Single-/Multi Agent Reinforcement Learning"""
+import numpy as np
+import rospy
+from time import sleep
+from typing import List, Tuple, Dict, Any, Union, Callable
+from gym import spaces
+from pettingzoo import *
+from pettingzoo.utils import from_parallel, to_parallel
+from warnings import warn
+
+import supersuit as ss
+
+from arena_marl.marl_agent.utils.supersuit_utils import MarkovVectorEnv_patched
+from arena_navigation.arena_local_planner.learning_based.arena_local_planner_drl.rl_agent.training_agent_wrapper import (
+ TrainingDRLAgent,
+)
+from task_generator.task_generator.marl_tasks import get_MARL_task
+from flatland_msgs.srv import StepWorld, StepWorldRequest
+
+# from marl_agent.utils.supersuit_utils import *
+# from rl_agent.utils.supersuit_utils import MarkovVectorEnv_patched
+
+
+def env_fn(**kwargs: Dict[str, Any]): # -> VecEnv:
+ """
+ The env function wraps the environment in 3 wrappers by default. These
+ wrappers contain logic that is common to many pettingzoo environments.
+ We recommend you use at least the OrderEnforcingWrapper on your own environment
+ to provide sane error messages. You can find full documentation for these methods
+ elsewhere in the developer documentation.
+ """
+ env = FlatlandPettingZooEnv(**kwargs)
+ env = from_parallel(env)
+ env = ss.pad_action_space_v0(env)
+ env = ss.pad_observations_v0(env)
+ env = to_parallel(env)
+ env = MarkovVectorEnv_patched(env, black_death=True)
+ return env
+
+
+class FlatlandPettingZooEnv(ParallelEnv):
+ """The SuperSuit Parallel environment steps every live agent at once."""
+
+ def __init__(
+ self,
+ num_agents: int,
+ agent_list_fn: Callable[[int, str, str, str, str], List[TrainingDRLAgent]],
+ PATHS: dict,
+ ns: str = None,
+ task_mode: str = "staged",
+ max_num_moves_per_eps: int = 1000,
+ agent_list_kwargs: Dict[str, Any] = None,
+ ) -> None:
+ """Initialization method for the Arena-Rosnav Pettingzoo Environment.
+
+ Args:
+ num_agents (int): Number of possible agents.
+ agent_list_fn (Callable[ [int, str, str, str, str], List[TrainingDRLAgent] ]): Initialization function for the agents. \
+ Returns a list of agent instances.
+ ns (str, optional): Environments' ROS namespace. There should only be one env per ns. Defaults to None.
+ task_mode (str, optional): Navigation task mode for the agents. Modes to chose from: ['random', 'staged']. \
+ Defaults to "random".
+ max_num_moves_per_eps (int, optional): Maximum number of moves per episode. Defaults to 1000.
+
+ Note:
+ These attributes should not be changed after initialization:
+ - possible_agents
+ - action_spaces
+ - observation_spaces
+ """
+ self._ns = "" if ns is None or not ns else f"{ns}/"
+ self._is_train_mode = rospy.get_param("/train_mode")
+ self.metadata = {}
+
+ self.agent_list: List[TrainingDRLAgent] = agent_list_fn(
+ num_agents, ns=ns, **(agent_list_kwargs or {})
+ )
+
+ self.agents = []
+ # list containing the unique robot namespaces
+ # used as identifier
+ self.possible_agents = [a._robot_sim_ns for a in self.agent_list]
+ self.agent_name_mapping = dict(
+ zip(self.possible_agents, list(range(len(self.possible_agents))))
+ )
+ self.agent_object_mapping = dict(zip(self.possible_agents, self.agent_list))
+ self.terminal_observation = {}
+
+ self._validate_agent_list()
+
+ # task manager
+ self.task_manager = get_MARL_task(
+ ns=ns,
+ mode=task_mode,
+ robot_ids=[a._robot_sim_ns for a in self.agent_list],
+ PATHS=PATHS,
+ )
+
+ # service clients
+ if self._is_train_mode:
+ self._service_name_step = f"{self._ns}step_world"
+ self._sim_step_client = rospy.ServiceProxy(
+ self._service_name_step, StepWorld
+ )
+
+ self._max_num_moves = max_num_moves_per_eps
+ self.action_provided, self.curr_actions = False, {}
+
+ def observation_space(self, agent: str) -> spaces.Box:
+ """Returns specific agents' observation space.
+
+ Args:
+ agent (str): Agent name as given in ``self.possible_agents``.
+
+ Returns:
+ spaces.Box: Observation space of type _gym.spaces_.
+ """
+ return self.agent_object_mapping[agent].observation_space
+
+ def action_space(self, agent: str) -> spaces.Box:
+ """Returns specific agents' action space.
+
+ Args:
+ agent (str): Agent name as given in ``self.possible_agents``.
+
+ Returns:
+ spaces.Box: Action space of type _gym.spaces_.
+ """
+ return self.agent_object_mapping[agent].action_space
+
+ def _validate_agent_list(self) -> None:
+ """Validates the agent list.
+
+ Description:
+ Checks if all agents are named differently. That means each robot adresses its own namespace.
+ """
+ assert len(self.possible_agents) == len(
+ set(self.possible_agents)
+ ), "Robot names and thus their namespaces, have to be unique!"
+
+ def reset(self) -> Dict[str, np.ndarray]:
+ """Resets the environment and returns the new set of observations (keyed by the agent name)
+
+ Description:
+ This method is called when all agents reach an end criterion. End criterions are: exceeding the \
+ max number of steps per episode, crash or reaching the end criterion.
+ The scene is then reseted.
+
+ Returns:
+ Dict[str, np.ndarray]: Observations dictionary in {_agent name_: _respective observations_}.
+ """
+ self.agents, self.num_moves, self.terminal_observation = (
+ self.possible_agents[:],
+ 0,
+ {},
+ )
+
+ # reset the reward calculator
+ for agent in self.agents:
+ self.agent_object_mapping[agent].reward_calculator.reset()
+
+ # reset the task manager
+ self.task_manager.reset()
+ # step one timestep in the simulation to update the scene
+ if self._is_train_mode:
+ self._sim_step_client()
+
+ # get first observations for the next episode
+ observations = {
+ agent: self.agent_object_mapping[agent].get_observations()[0]
+ for agent in self.agents
+ }
+
+ self.action_provided, self.curr_actions = False, {}
+ return observations
+
+ def step(
+ self, actions: Dict[str, np.ndarray]
+ ) -> Tuple[
+ Dict[str, np.ndarray],
+ Dict[str, float],
+ Dict[str, bool],
+ Dict[str, Dict[str, Any]],
+ ]:
+ """Simulates one timestep and returns the most recent environment information.
+
+ Description:
+ This function takes in velocity commands and applies those to the simulation.
+ Afterwards, agents' observations are retrieved from the current timestep and \
+ the reward is calculated. \
+ Proceeding with the ``RewardCalculator`` processing the observations and detecting certain events like \
+ if a crash occured, a goal was reached. Those informations are returned in the '*reward\_info*' \
+ which itself is a dictionary. \
+ Eventually, dictionaries containing every agents' observations, rewards, done flags and \
+ episode information is returned.
+
+ Args:
+ actions (Dict[str, np.ndarray]): Actions dictionary in {_agent name_: _respective observations_}.
+
+ Returns:
+ Tuple[ Dict[str, np.ndarray], Dict[str, float], Dict[str, bool], Dict[str, Dict[str, Any]], ]: Observations, \
+ rewards, done flags and episode informations dictionary.
+
+ Note:
+ Done reasons are mapped as follows: __0__ - episode length exceeded, __1__ - agent crashed, \
+ __2__ - agent reached its goal.
+ """
+ # If a user passes in actions with no agents, then just return empty observations, etc.
+ if not actions:
+ self.agents = []
+ return {}, {}, {}, {}
+
+ # actions
+ for agent in self.possible_agents:
+ if agent in actions:
+ self.agent_object_mapping[agent].publish_action(actions[agent])
+ else:
+ noop = np.zeros(shape=self.action_space(agent).shape)
+ self.agent_object_mapping[agent].publish_action(noop)
+
+ # fast-forward simulation
+ self.call_service_takeSimStep()
+ self.num_moves += 1
+
+ merged_obs, rewards, reward_infos = {}, {}, {}
+
+ for agent in actions:
+ # observations
+ merged, _dict = self.agent_object_mapping[agent].get_observations()
+ merged_obs[agent] = merged
+
+ # rewards and infos
+ reward, reward_info = self.agent_object_mapping[agent].get_reward(
+ action=actions[agent], obs_dict=_dict
+ )
+ rewards[agent], reward_infos[agent] = reward, reward_info
+
+ # dones & infos
+ dones, infos = self._get_dones(reward_infos), self._get_infos(reward_infos)
+
+ # remove done agents from the active agents list
+ self.agents = [agent for agent in self.agents if not dones[agent]]
+
+ for agent in self.possible_agents:
+ # agent is done in this episode
+ if agent in dones and dones[agent]:
+ self.terminal_observation[agent] = merged_obs[agent]
+ infos[agent]["terminal_observation"] = merged_obs[agent]
+ # agent is done since atleast last episode
+ elif agent not in self.agents:
+ if agent not in infos:
+ infos[agent] = {}
+ infos[agent]["terminal_observation"] = self.terminal_observation[agent]
+
+ return merged_obs, rewards, dones, infos
+
+ def apply_action(self, actions: np.ndarray) -> None:
+ """_summary_
+
+ Args:
+ action (np.ndarray): _description_
+
+ Returns:
+ _type_: _description_
+ """
+ if self.action_provided:
+ warn(
+ "Disobeyed method order. Called 'apply_action' multiple times without retrieving states."
+ )
+
+ # If a user passes in actions with no agents, then just return empty observations, etc.
+ if not actions:
+ self.agents = []
+ return {}, {}, {}, {}
+
+ # actions
+ for agent in self.possible_agents:
+ if agent in actions:
+ self.agent_object_mapping[agent].publish_action(actions[agent])
+ else:
+ noop = np.zeros(shape=self.action_space(agent).shape)
+ self.agent_object_mapping[agent].publish_action(noop)
+
+ self.num_moves += 1
+ self.action_provided, self.curr_actions = True, actions
+
+ def get_states(
+ self,
+ ) -> Tuple[
+ Dict[str, np.ndarray], Dict[str, float], Dict[str, bool], Dict[str, Any]
+ ]:
+ assert self.action_provided, "No actions provided"
+ merged_obs, rewards, reward_infos = {}, {}, {}
+
+ for agent in self.curr_actions:
+ # observations
+ merged, _dict = self.agent_object_mapping[agent].get_observations()
+ merged_obs[agent] = merged
+
+ # rewards and infos
+ reward, reward_info = self.agent_object_mapping[agent].get_reward(
+ action=self.curr_actions[agent], obs_dict=_dict
+ )
+ rewards[agent], reward_infos[agent] = reward, reward_info
+
+ # dones & infos
+ dones, infos = self._get_dones(reward_infos), self._get_infos(reward_infos)
+
+ # remove done agents from the active agents list
+ self.agents = [agent for agent in self.agents if not dones[agent]]
+
+ for agent in self.possible_agents:
+ # agent is done in this episode
+ if agent in dones and dones[agent]:
+ self.terminal_observation[agent] = merged_obs[agent]
+ infos[agent]["terminal_observation"] = merged_obs[agent]
+ # agent is done since atleast last episode
+ elif agent not in self.agents:
+ if agent not in infos:
+ infos[agent] = {}
+ infos[agent]["terminal_observation"] = self.terminal_observation[agent]
+
+ return merged_obs, rewards, dones, infos
+
+ @property
+ def max_num_agents(self):
+ return len(self.agents)
+
+ def call_service_takeSimStep(self, t: float = None):
+ """Fast-forwards the simulation.
+
+ Description:
+ Simulates the Flatland simulation for a certain amount of seconds.
+
+ Args:
+ t (float, optional): Time in seconds. When ``t`` is None, time is forwarded by ``step_size`` s \
+ (ROS parameter). Defaults to None.
+ """
+ request = StepWorldRequest() if t is None else StepWorldRequest(t)
+
+ try:
+ response = self._sim_step_client(request)
+ rospy.logdebug("step service=", response)
+ except rospy.ServiceException as e:
+ rospy.logdebug(f"step Service call failed: {e}")
+
+ def _get_dones(self, reward_infos: Dict[str, Dict[str, Any]]) -> Dict[str, bool]:
+ """Extracts end flags from the reward information dictionary.
+
+ Args:
+ reward_infos (Dict[str, Dict[str, Any]]): Episode information from the ``RewardCalculator`` in \
+ {_agent name_: _reward infos_}.
+
+ Returns:
+ Dict[str, bool]: Dones dictionary in {_agent name_: _done flag_}
+
+ Note:
+ Relevant dictionary keys are: "is_done", "is_success", "done_reason"
+ """
+ return (
+ {agent: reward_infos[agent]["is_done"] for agent in self.agents}
+ if self.num_moves < self._max_num_moves
+ else {agent: True for agent in self.agents}
+ )
+
+ def _get_infos(
+ self, reward_infos: Dict[str, Dict[str, Any]]
+ ) -> Dict[str, Dict[str, Any]]:
+ """Extracts the current episode information from the reward information dictionary.
+
+ Args:
+ reward_infos (Dict[str, Dict[str, Any]]): Episode information from the ``RewardCalculator`` in \
+ {_agent name_: _reward infos_}.
+
+ Returns:
+ Dict[str, Dict[str, Any]]: Info dictionary in {_agent name_: _done flag_}
+
+ Note:
+ Relevant dictionary keys are: "is_done", "is_success", "done_reason"
+ """
+ infos = {agent: {} for agent in self.agents}
+ for agent in self.agents:
+ if reward_infos[agent]["is_done"]:
+ infos[agent] = reward_infos[agent]
+ elif self.num_moves >= self._max_num_moves:
+ infos[agent] = {
+ "done_reason": 0,
+ "is_success": 0,
+ }
+ return infos
diff --git a/arena_marl/marl_agent/utils/__init__.py b/arena_marl/marl_agent/utils/__init__.py
new file mode 100755
index 000000000..e69de29bb
diff --git a/arena_marl/marl_agent/utils/action_collector.py b/arena_marl/marl_agent/utils/action_collector.py
new file mode 100755
index 000000000..7c95eea27
--- /dev/null
+++ b/arena_marl/marl_agent/utils/action_collector.py
@@ -0,0 +1,68 @@
+import rospy
+import numpy as np
+
+from tf.transformations import *
+from geometry_msgs.msg import Twist
+from gym import spaces
+
+
+class ActionCollector:
+ def __init__(self):
+ self.v_max_ = 0.8
+ self.w_max_ = 1.2
+ self.action_library = {
+ 0: {"linear": 0.0, "angular": -self.w_max_},
+ 1: {"linear": self.v_max_, "angular": 0.0},
+ 2: {"linear": 0.0, "angular": self.w_max_},
+ 3: {"linear": self.v_max_, "angular": self.w_max_ / 2},
+ 4: {"linear": self.v_max_, "angular": -self.w_max_ / 2},
+ 5: {"linear": 0.0, "angular": 0.0},
+ }
+ self.N_DISCRETE_ACTIONS = len(self.action_library)
+ self.action_space = spaces.Discrete(self.N_DISCRETE_ACTIONS)
+
+ def get_action_space(self):
+ return self.action_space
+
+ def get_cmd_vel(self, action_id):
+ vel_msg = Twist()
+ vel_msg.linear.x = self.action_library[action_id]["linear"]
+ vel_msg.angular.z = self.action_library[action_id]["angular"]
+ return vel_msg
+
+
+if __name__ == "__main__":
+ action_collector = ActionCollector()
+ print(action_collector.get_cmd_vel(1))
+
+ box = spaces.Box(low=3.0, high=4, shape=(2, 2))
+ print(box)
+ box.seed(4)
+ for _ in range(1):
+ print(box.sample())
+
+ min_position = 0
+ max_position = 10
+ max_speed = 2
+ goal_position = 0.5
+ low = np.array([min_position, -max_speed])
+ high = np.array([max_position, max_speed])
+ action_space = spaces.Discrete(3) # action space
+ observation_space = spaces.Box(low, high) #
+ print("*" * 10)
+ print(observation_space)
+ for _ in range(2):
+ print(observation_space.sample())
+
+ observation_space = spaces.Tuple(
+ (
+ spaces.Box(low=0, high=10, shape=(10,), dtype=np.float32),
+ spaces.Box(low=-10, high=0, shape=(3 + 2,), dtype=np.float32),
+ )
+ )
+ print("2" * 10)
+ print(observation_space.sample())
+ print(type(observation_space.sample()))
+
+ reward = spaces.Discrete(4)
+ print(type(reward.sample()))
diff --git a/arena_marl/marl_agent/utils/debug.py b/arena_marl/marl_agent/utils/debug.py
new file mode 100755
index 000000000..0349734d2
--- /dev/null
+++ b/arena_marl/marl_agent/utils/debug.py
@@ -0,0 +1,16 @@
+import time
+from functools import wraps
+
+
+def timeit(f):
+ @wraps(f)
+ def timed(*args, **kw):
+
+ ts = time.time()
+ result = f(*args, **kw)
+ te = time.time()
+
+ print("func:%r args:[%r, %r] took: %2.4f sec" % (f.__name__, args, kw, te - ts))
+ return result
+
+ return timed
diff --git a/arena_marl/marl_agent/utils/observation_collector.py b/arena_marl/marl_agent/utils/observation_collector.py
new file mode 100755
index 000000000..7181ed443
--- /dev/null
+++ b/arena_marl/marl_agent/utils/observation_collector.py
@@ -0,0 +1,300 @@
+#! /usr/bin/env python
+from typing import Tuple
+
+from numpy.core.numeric import normalize_axis_tuple
+import rospy
+import random
+import numpy as np
+from collections import deque
+
+import time # for debuging
+import threading
+
+# observation msgs
+from sensor_msgs.msg import LaserScan
+from geometry_msgs.msg import Pose2D, PoseStamped, Twist
+from nav_msgs.msg import Path
+from rosgraph_msgs.msg import Clock
+from nav_msgs.msg import Odometry
+
+# message filter
+import message_filters
+
+# for transformations
+from tf.transformations import *
+
+from gym import spaces
+import numpy as np
+
+
+class ObservationCollector:
+ def __init__(
+ self,
+ ns: str,
+ num_lidar_beams: int,
+ lidar_range: float,
+ ):
+ """a class to collect and merge observations
+
+ Args:
+ num_lidar_beams (int): [description]
+ lidar_range (float): [description]
+ """
+ """SET A METHOD TO EXTRACT IF MARL IS BEIN REQUESTED"""
+ MARL = rospy.get_param("num_robots") > 1
+
+ self.ns = ns
+ if ns is None or ns == "":
+ self.ns_prefix = ""
+ else:
+ self.ns_prefix = (
+ "/" + ns + "/" if not ns.endswith("/") else "/" + ns
+ )
+
+ # define observation_space
+ self.observation_space = ObservationCollector._stack_spaces(
+ (
+ spaces.Box(
+ low=0,
+ high=lidar_range,
+ shape=(num_lidar_beams,),
+ dtype=np.float32,
+ ),
+ spaces.Box(low=0, high=10, shape=(1,), dtype=np.float32),
+ spaces.Box(
+ low=-np.pi, high=np.pi, shape=(1,), dtype=np.float32
+ ),
+ )
+ )
+
+ self._laser_num_beams = num_lidar_beams
+
+ self._clock = Clock()
+ self._scan = LaserScan()
+ self._robot_pose = Pose2D()
+ self._robot_vel = Twist()
+ self._subgoal = Pose2D()
+ self._globalplan = np.array([])
+
+ # subscriptions
+ self._scan_sub = rospy.Subscriber(
+ f"{self.ns_prefix}scan",
+ LaserScan,
+ self.callback_scan,
+ tcp_nodelay=True,
+ )
+ self._robot_state_sub = rospy.Subscriber(
+ f"{self.ns_prefix}odom",
+ Odometry,
+ self.callback_robot_state,
+ tcp_nodelay=True,
+ )
+
+ # self._clock_sub = rospy.Subscriber(
+ # f'{self.ns_prefix}clock', Clock, self.callback_clock, tcp_nodelay=True)
+
+ # when using MARL listen to goal directly since no intermediate planner is considered
+ goal_topic = (
+ f"{self.ns_prefix}goal" if MARL else f"{self.ns_prefix}subgoal"
+ )
+ self._subgoal_sub = rospy.Subscriber(
+ f"{goal_topic}", PoseStamped, self.callback_subgoal
+ )
+ self._globalplan_sub = rospy.Subscriber(
+ f"{self.ns_prefix}globalPlan", Path, self.callback_global_plan
+ )
+
+ # synchronization parameters
+ self._first_sync_obs = (
+ True # whether to return first sync'd obs or most recent
+ )
+ self.max_deque_size = 10
+ self._sync_slop = 0.1
+
+ self._laser_deque = deque()
+ self._rs_deque = deque()
+
+ def get_observation_space(self):
+ return self.observation_space
+
+ def get_observations(self):
+ # try to retrieve sync'ed obs
+ laser_scan, robot_pose = self.get_sync_obs()
+ if laser_scan is not None and robot_pose is not None:
+ # print("Synced successfully")
+ self._scan = laser_scan
+ self._robot_pose = robot_pose
+ # else:
+ # print("Not synced")
+ if len(self._scan.ranges) > 0:
+ scan = self._scan.ranges.astype(np.float32)
+ else:
+ scan = np.zeros(self._laser_num_beams, dtype=float)
+
+ rho, theta = ObservationCollector._get_goal_pose_in_robot_frame(
+ self._subgoal, self._robot_pose
+ )
+ merged_obs = np.hstack([scan, np.array([rho, theta])])
+
+ obs_dict = {
+ "laser_scan": scan,
+ "goal_in_robot_frame": [rho, theta],
+ "global_plan": self._globalplan,
+ "robot_pose": self._robot_pose,
+ }
+
+ self._laser_deque.clear()
+ self._rs_deque.clear()
+ return merged_obs, obs_dict
+
+ @staticmethod
+ def _get_goal_pose_in_robot_frame(
+ goal_pos: Pose2D, robot_pos: Pose2D
+ ) -> Tuple[float, float]:
+ y_relative = goal_pos.y - robot_pos.y
+ x_relative = goal_pos.x - robot_pos.x
+ rho = (x_relative ** 2 + y_relative ** 2) ** 0.5
+ theta = (
+ np.arctan2(y_relative, x_relative) - robot_pos.theta + 4 * np.pi
+ ) % (2 * np.pi) - np.pi
+ return rho, theta
+
+ def get_sync_obs(self):
+ laser_scan = None
+ robot_pose = None
+
+ # print(f"laser deque: {len(self._laser_deque)}, robot state deque: {len(self._rs_deque)}")
+ while len(self._rs_deque) > 0 and len(self._laser_deque) > 0:
+ laser_scan_msg = self._laser_deque.popleft()
+ robot_pose_msg = self._rs_deque.popleft()
+
+ laser_stamp = laser_scan_msg.header.stamp.to_sec()
+ robot_stamp = robot_pose_msg.header.stamp.to_sec()
+
+ while abs(laser_stamp - robot_stamp) > self._sync_slop:
+ if laser_stamp > robot_stamp:
+ if len(self._rs_deque) == 0:
+ return laser_scan, robot_pose
+ robot_pose_msg = self._rs_deque.popleft()
+ robot_stamp = robot_pose_msg.header.stamp.to_sec()
+ else:
+ if len(self._laser_deque) == 0:
+ return laser_scan, robot_pose
+ laser_scan_msg = self._laser_deque.popleft()
+ laser_stamp = laser_scan_msg.header.stamp.to_sec()
+
+ laser_scan = self.process_scan_msg(laser_scan_msg)
+ robot_pose, _ = self.process_robot_state_msg(robot_pose_msg)
+
+ if self._first_sync_obs:
+ break
+
+ # print(f"Laser_stamp: {laser_stamp}, Robot_stamp: {robot_stamp}")
+ return laser_scan, robot_pose
+
+ def callback_clock(self, msg_Clock):
+ self._clock = msg_Clock.clock.to_sec()
+ return
+
+ def callback_subgoal(self, msg_Subgoal):
+ self._subgoal = self.process_subgoal_msg(msg_Subgoal)
+ return
+
+ def callback_global_plan(self, msg_global_plan):
+ self._globalplan = ObservationCollector.process_global_plan_msg(
+ msg_global_plan
+ )
+ return
+
+ def callback_scan(self, msg_laserscan):
+ if len(self._laser_deque) == self.max_deque_size:
+ self._laser_deque.popleft()
+ self._laser_deque.append(msg_laserscan)
+
+ def callback_robot_state(self, msg_robotstate):
+ if len(self._rs_deque) == self.max_deque_size:
+ self._rs_deque.popleft()
+ self._rs_deque.append(msg_robotstate)
+
+ def callback_observation_received(
+ self, msg_LaserScan, msg_RobotStateStamped
+ ):
+ # process sensor msg
+ self._scan = self.process_scan_msg(msg_LaserScan)
+ self._robot_pose, self._robot_vel = self.process_robot_state_msg(
+ msg_RobotStateStamped
+ )
+ self.obs_received = True
+ return
+
+ def process_scan_msg(self, msg_LaserScan: LaserScan):
+ # remove_nans_from_scan
+ self._scan_stamp = msg_LaserScan.header.stamp.to_sec()
+ scan = np.array(msg_LaserScan.ranges)
+ scan[np.isnan(scan)] = msg_LaserScan.range_max
+ msg_LaserScan.ranges = scan
+ return msg_LaserScan
+
+ def process_robot_state_msg(self, msg_Odometry):
+ pose3d = msg_Odometry.pose.pose
+ twist = msg_Odometry.twist.twist
+ return self.pose3D_to_pose2D(pose3d), twist
+
+ def process_pose_msg(self, msg_PoseWithCovarianceStamped):
+ # remove Covariance
+ pose_with_cov = msg_PoseWithCovarianceStamped.pose
+ pose = pose_with_cov.pose
+ return self.pose3D_to_pose2D(pose)
+
+ def process_subgoal_msg(self, msg_Subgoal):
+ return self.pose3D_to_pose2D(msg_Subgoal.pose)
+
+ @staticmethod
+ def process_global_plan_msg(globalplan):
+ global_plan_2d = list(
+ map(
+ lambda p: ObservationCollector.pose3D_to_pose2D(p.pose),
+ globalplan.poses,
+ )
+ )
+ return np.array(list(map(lambda p2d: [p2d.x, p2d.y], global_plan_2d)))
+
+ @staticmethod
+ def pose3D_to_pose2D(pose3d):
+ pose2d = Pose2D()
+ pose2d.x = pose3d.position.x
+ pose2d.y = pose3d.position.y
+ quaternion = (
+ pose3d.orientation.x,
+ pose3d.orientation.y,
+ pose3d.orientation.z,
+ pose3d.orientation.w,
+ )
+ euler = euler_from_quaternion(quaternion)
+ yaw = euler[2]
+ pose2d.theta = yaw
+ return pose2d
+
+ @staticmethod
+ def _stack_spaces(ss: Tuple[spaces.Box]):
+ low = []
+ high = []
+ for space in ss:
+ low.extend(space.low.tolist())
+ high.extend(space.high.tolist())
+ return spaces.Box(np.array(low).flatten(), np.array(high).flatten())
+
+
+if __name__ == "__main__":
+
+ rospy.init_node("states", anonymous=True)
+ print("start")
+
+ state_collector = ObservationCollector("sim1/", 360, 10)
+ i = 0
+ r = rospy.Rate(100)
+ while i <= 1000:
+ i = i + 1
+ obs = state_collector.get_observations()
+
+ time.sleep(0.001)
diff --git a/arena_marl/marl_agent/utils/reward.py b/arena_marl/marl_agent/utils/reward.py
new file mode 100755
index 000000000..d4b53cb16
--- /dev/null
+++ b/arena_marl/marl_agent/utils/reward.py
@@ -0,0 +1,472 @@
+"""Reward Calculator for DRL"""
+import numpy as np
+import scipy.spatial
+
+from geometry_msgs.msg import Pose2D
+from typing import Dict, Tuple, Union
+
+from rl_agent.utils.reward import RewardCalculator
+
+class RewardCalculator(RewardCalculator):
+ def __init__(
+ self,
+ robot_radius: float,
+ safe_dist: float,
+ goal_radius: float,
+ rule: str = "rule_00",
+ extended_eval: bool = False,
+ ):
+ """A facotry class for reward calculation. Holds various reward functions.
+
+ An overview of the reward functions can be found under:
+ https://github.com/ignc-research/arena-rosnav/blob/local_planner_subgoalmode/docs/DRL-Training.md#reward-functions
+
+ Possible reward functions: "_rule_00_", "_rule_01_", "_rule_02_", "_rule_03_", "_rule_04_"
+
+ Args:
+ robot_radius (float): Robots' radius in meters.
+ safe_dist (float): Robots' safe distance in meters.
+ goal_radius (float): Radius of the goal.
+ rule (str, optional): The desired reward function name. Defaults to "rule_00".
+ extended_eval (bool, optional): Extended evaluation mode. Defaults to False.
+ """
+ self.curr_reward = 0
+ # additional info will be stored here and be returned alonge with reward.
+ self.info = {}
+ self.robot_radius = robot_radius
+ self.goal_radius = goal_radius
+ self.last_goal_dist = None
+ self.last_dist_to_path = None
+ self.last_action = None
+ self.safe_dist = robot_radius + safe_dist
+ self._extended_eval = extended_eval
+
+ self.kdtree = None
+
+ self._cal_funcs = {
+ "rule_00": RewardCalculator._cal_reward_rule_00,
+ "rule_01": RewardCalculator._cal_reward_rule_01,
+ "rule_02": RewardCalculator._cal_reward_rule_02,
+ "rule_03": RewardCalculator._cal_reward_rule_03,
+ "rule_04": RewardCalculator._cal_reward_rule_04,
+ }
+ self.cal_func = self._cal_funcs[rule]
+
+ def reset(self) -> None:
+ """Resets variables related to the episode."""
+ self.last_goal_dist = None
+ self.last_dist_to_path = None
+ self.last_action = None
+ self.kdtree = None
+
+ def _reset(self) -> None:
+ """Resets variables related to current step."""
+ self.curr_reward = 0
+ self.info = {}
+
+ def get_reward(
+ self,
+ laser_scan: np.ndarray,
+ goal_in_robot_frame: Tuple[float, float],
+ *args,
+ **kwargs
+ ) -> Tuple[float, Dict[str, Union[str, int, bool]]]:
+ """Returns reward and info to the gym environment.
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data.
+ goal_in_robot_frame (Tuple[float, float]): Position (rho, theta) of the goal in the robot frame (polar coordinate).
+
+ Returns:
+ Tuple[float, Dict[str, Union[str, int, bool]]]: Tuple of calculated rewards for the current step, \
+ and the reward information dictionary.
+ """
+ self._reset()
+ self.cal_func(self, laser_scan, goal_in_robot_frame, *args, **kwargs)
+ return self.curr_reward, self.info
+
+ def _cal_reward_rule_00(
+ self,
+ laser_scan: np.ndarray,
+ goal_in_robot_frame: Tuple[float, float],
+ *args,
+ **kwargs
+ ):
+ """Reward function: '_rule\_00_'
+
+ Description:
+ "rule_00" incorporates the most instinctive characteristics for learning navigation into its \
+ reward calculation. The reward function is made up of only 4 summands, namely the success \
+ reward, the collision reward, the danger reward and the progress reward. Similar reward functions \
+ were utilized in numerous research projects and produced promising results. Thus, this \
+ rule is chosen to be the basis for further experiments with extended versions of it. \
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data.
+ goal_in_robot_frame (Tuple[float, float]): Position (rho, theta) of the goal in the robot frame (polar coordinate).
+ """
+ self._reward_goal_reached(goal_in_robot_frame)
+ self._reward_safe_dist(laser_scan, punishment=0.25)
+ self._reward_collision(laser_scan)
+ self._reward_goal_approached(
+ goal_in_robot_frame, reward_factor=0.3, penalty_factor=0.4
+ )
+
+ def _cal_reward_rule_01(
+ self,
+ laser_scan: np.ndarray,
+ goal_in_robot_frame: Tuple[float, float],
+ *args,
+ **kwargs
+ ):
+ """Reward function: '_rule\_01_'
+
+ Description:
+ This reward function extends "rule 00" by adding a penalty factor that affects the current \
+ reward like an abstract fuel consumption factor. In principle, a certain penalty is applied \
+ for each action taken depending on the velocity and thus imposes a severer punishment for \
+ dissipated driving.
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data.
+ goal_in_robot_frame (Tuple[float, float]): Position (rho, theta) of the goal in the robot frame (polar coordinate).
+ """
+ self._reward_distance_traveled(
+ kwargs["action"], consumption_factor=0.0075
+ )
+ self._reward_goal_reached(goal_in_robot_frame, reward=15)
+ self._reward_safe_dist(laser_scan, punishment=0.25)
+ self._reward_collision(laser_scan, punishment=10)
+ self._reward_goal_approached(
+ goal_in_robot_frame, reward_factor=0.3, penalty_factor=0.4
+ )
+
+ def _cal_reward_rule_02(
+ self,
+ laser_scan: np.ndarray,
+ goal_in_robot_frame: Tuple[float, float],
+ *args,
+ **kwargs
+ ):
+ """Reward function: '_rule\_02_'
+
+ Description:
+ Previous reward functions required only basic information from the simulation. For this rule, \
+ which builds on the reward function "rule 01", we introduced the assessment of the progress \
+ regarding the global plan. The additional summand essentially rewards the agent for following \
+ the global plan. It was implemented in order to test the effect of including the global plan in \
+ the reward calculation. \
+ Since "rule 02" shares almost the same reward function composition as "rule 01", similar performance \
+ was expected to some extent. The desired behavior for this agent was to learn faster and \
+ to drive more goal-oriented than the agent of "rule 01", as this rule was provided the global plan. \
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data.
+ goal_in_robot_frame (Tuple[float, float]): Position (rho, theta) of the goal in the robot frame (polar coordinate).
+ """
+ self._reward_distance_traveled(
+ kwargs["action"], consumption_factor=0.0075
+ )
+ self._reward_following_global_plan(
+ kwargs["global_plan"], kwargs["robot_pose"]
+ )
+ self._reward_goal_reached(goal_in_robot_frame, reward=15)
+ self._reward_safe_dist(laser_scan, punishment=0.25)
+ self._reward_collision(laser_scan, punishment=10)
+ self._reward_goal_approached(
+ goal_in_robot_frame, reward_factor=0.3, penalty_factor=0.4
+ )
+
+ def _cal_reward_rule_03(
+ self,
+ laser_scan: np.ndarray,
+ goal_in_robot_frame: Tuple[float, float],
+ *args,
+ **kwargs
+ ):
+ """Reward function: '_rule\_03_'
+
+ Description:
+ The base of this rule is made up of summands from "rule 00". The two extra factors were \
+ introduced in order to further leverage the global plan information for reward generation. \
+ One that rewards the following of the global path and one for valuing the agents’ action - \
+ positively, when it approaches the global plan - negatively when the robot distances itself \
+ from the path. \
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data. \
+ goal_in_robot_frame (Tuple[float, float]): Position (rho, theta) of the goal in the robot frame (polar coordinate). \
+ """
+ self._reward_following_global_plan(
+ kwargs["global_plan"], kwargs["robot_pose"], kwargs["action"]
+ )
+ if laser_scan.min() > self.safe_dist:
+ self._reward_distance_global_plan(
+ kwargs["global_plan"],
+ kwargs["robot_pose"],
+ reward_factor=0.2,
+ penalty_factor=0.3,
+ )
+ else:
+ self.last_dist_to_path = None
+ self._reward_goal_reached(goal_in_robot_frame, reward=15)
+ self._reward_safe_dist(laser_scan, punishment=0.25)
+ self._reward_collision(laser_scan, punishment=10)
+ self._reward_goal_approached(
+ goal_in_robot_frame, reward_factor=0.3, penalty_factor=0.4
+ )
+
+ def _cal_reward_rule_04(
+ self,
+ laser_scan: np.ndarray,
+ goal_in_robot_frame: Tuple[float, float],
+ *args,
+ **kwargs
+ ):
+ """Reward function: '_rule\_04_'
+
+ Description:
+ This reward function extends "rule 03" with an additional term that punishes the agent for \
+ abruptly changing the direction. Previous test runs, conducted right after the implementation, \
+ evidenced that although the agent performed well on different tasks, the robot tended to drive \
+ in tail motion. It was aimed to adjust this behavior by including this additional penalty term. \
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data.
+ goal_in_robot_frame (Tuple[float, float]): Position (rho, theta) of the goal in the robot frame (polar coordinate).
+ """
+ self._reward_abrupt_direction_change(kwargs["action"])
+ self._reward_following_global_plan(
+ kwargs["global_plan"], kwargs["robot_pose"], kwargs["action"]
+ )
+ if laser_scan.min() > self.safe_dist:
+ self._reward_distance_global_plan(
+ kwargs["global_plan"],
+ kwargs["robot_pose"],
+ reward_factor=0.2,
+ penalty_factor=0.3,
+ )
+ else:
+ self.last_dist_to_path = None
+ self._reward_goal_reached(goal_in_robot_frame, reward=15)
+ self._reward_safe_dist(laser_scan, punishment=0.25)
+ self._reward_collision(laser_scan, punishment=10)
+ self._reward_goal_approached(
+ goal_in_robot_frame, reward_factor=0.3, penalty_factor=0.4
+ )
+
+ def _reward_goal_reached(
+ self, goal_in_robot_frame: Tuple[float, float], reward: float = 15
+ ):
+ """Reward for reaching the goal.
+
+ Args:
+ goal_in_robot_frame (Tuple[float, float], optional): Position (rho, theta) of the goal in the robot frame (polar coordinate).
+ reward (float, optional): Reward amount for reaching the goal. Defaults to 15.
+ """
+ if goal_in_robot_frame[0] < self.goal_radius:
+ self.curr_reward = reward
+ self.info["is_done"] = True
+ self.info["done_reason"] = 2
+ self.info["is_success"] = 1
+ else:
+ self.info["is_done"] = False
+
+ def _reward_goal_approached(
+ self,
+ goal_in_robot_frame=Tuple[float, float],
+ reward_factor: float = 0.3,
+ penalty_factor: float = 0.5,
+ ):
+ """Reward for approaching the goal.
+
+ Args:
+ goal_in_robot_frame ([type], optional): Position (rho, theta) of the goal in the robot frame (polar coordinate). Defaults to Tuple[float, float].
+ reward_factor (float, optional): Factor to be multiplied when the difference between current distance to goal and the previous one is positive. \
+ Defaults to 0.3.
+ penalty_factor (float, optional): Factor to be multiplied when the difference between current distance to goal and the previous one is negative. Defaults to 0.5.
+ """
+ if self.last_goal_dist is not None:
+ # goal_in_robot_frame : [rho, theta]
+
+ # higher negative weight when moving away from goal
+ # (to avoid driving unnecessary circles when train in contin. action space)
+ if (self.last_goal_dist - goal_in_robot_frame[0]) > 0:
+ w = reward_factor
+ else:
+ w = penalty_factor
+ reward = w * (self.last_goal_dist - goal_in_robot_frame[0])
+
+ # print("reward_goal_approached: {}".format(reward))
+ self.curr_reward += reward
+ self.last_goal_dist = goal_in_robot_frame[0]
+
+ def _reward_collision(self, laser_scan: np.ndarray, punishment: float = 10):
+ """Reward for colliding with an obstacle.
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data.
+ punishment (float, optional): Punishment amount for collisions. Defaults to 10.
+ """
+ if laser_scan.min() <= self.robot_radius:
+ self.curr_reward -= punishment
+
+ if not self._extended_eval:
+ self.info["is_done"] = True
+ self.info["done_reason"] = 1
+ self.info["is_success"] = 0
+ else:
+ self.info["crash"] = True
+
+ def _reward_safe_dist(
+ self, laser_scan: np.ndarray, punishment: float = 0.15
+ ):
+ """Reward for undercutting safe distance.
+
+ Args:
+ laser_scan (np.ndarray): 2D laser scan data.
+ punishment (float, optional): Punishment amount. Could be applied in consecutive timesteps. \
+ Defaults to 0.15.
+ """
+ if laser_scan.min() < self.safe_dist:
+ self.curr_reward -= punishment
+
+ if self._extended_eval:
+ self.info["safe_dist"] = True
+
+ def _reward_not_moving(
+ self, action: np.ndarray = None, punishment: float = 0.01
+ ):
+ """Reward for not moving.
+
+ Args:
+ action (np.ndarray, optional): Array of shape (2,). First entry, linear velocity. \
+ Second entry, angular velocity. Defaults to None.
+ punishment (float, optional): Punishment for not moving. Defaults to 0.01.
+
+ Note:
+ Only applies half of the punishment amount when angular velocity is larger than zero.
+ """
+ if action is not None and action[0] == 0.0:
+ self.curr_reward -= (
+ punishment if action[1] == 0.0 else punishment / 2
+ )
+
+ def _reward_distance_traveled(
+ self,
+ action: np.array = None,
+ punishment: float = 0.01,
+ consumption_factor: float = 0.005,
+ ):
+ """Reward for driving a certain distance. Supposed to represent "fuel consumption".
+
+ Args:
+ action (np.array, optional): Array of shape (2,). First entry, linear velocity. \
+ Second entry, angular velocity. Defaults to None.
+ punishment (float, optional): Punishment when action can't be retrieved. Defaults to 0.01.
+ consumption_factor (float, optional): Factor for the weighted velocity punishment. Defaults to 0.005.
+ """
+ if action is None:
+ self.curr_reward -= punishment
+ else:
+ lin_vel = action[0]
+ ang_vel = action[1]
+ reward = (lin_vel + (ang_vel * 0.001)) * consumption_factor
+ self.curr_reward -= reward
+
+ def _reward_distance_global_plan(
+ self,
+ global_plan: np.array,
+ robot_pose: Pose2D,
+ reward_factor: float = 0.1,
+ penalty_factor: float = 0.15,
+ ):
+ """Reward for approaching/veering away the global plan.
+
+ Description:
+ Weighted difference between prior distance to global plan and current distance to global plan.
+
+ Args:
+ global_plan (np.array): Array containing 2D poses.
+ robot_pose (Pose2D): Robot position.
+ reward_factor (float, optional): Factor to be multiplied when the difference between current \
+ distance to global plan and the previous one is positive. Defaults to 0.1.
+ penalty_factor (float, optional): Factor to be multiplied when the difference between current \
+ distance to global plan and the previous one is negative. Defaults to 0.15.
+ """
+ if global_plan is not None and len(global_plan) != 0:
+ curr_dist_to_path, idx = self.get_min_dist2global_kdtree(
+ global_plan, robot_pose
+ )
+
+ if self.last_dist_to_path is not None:
+ if curr_dist_to_path < self.last_dist_to_path:
+ w = reward_factor
+ else:
+ w = penalty_factor
+
+ self.curr_reward += w * (
+ self.last_dist_to_path - curr_dist_to_path
+ )
+ self.last_dist_to_path = curr_dist_to_path
+
+ def _reward_following_global_plan(
+ self,
+ global_plan: np.array,
+ robot_pose: Pose2D,
+ action: np.array = None,
+ dist_to_path: float = 0.5,
+ ):
+ """Reward for travelling along the global plan.
+
+ Args:
+ global_plan (np.array): Array containing 2D poses.
+ robot_pose (Pose2D): Robot position.
+ action (np.array, optional): action (np.ndarray, optional): Array of shape (2,). First entry, linear velocity. \
+ Second entry, angular velocity. Defaults to None.
+ dist_to_path (float, optional): Minimum distance to the global path. Defaults to 0.5.
+ """
+ if (
+ global_plan is not None
+ and len(global_plan) != 0
+ and action is not None
+ ):
+ curr_dist_to_path, idx = self.get_min_dist2global_kdtree(
+ global_plan, robot_pose
+ )
+
+ if curr_dist_to_path <= dist_to_path:
+ self.curr_reward += 0.1 * action[0]
+
+ def get_min_dist2global_kdtree(
+ self, global_plan: np.array, robot_pose: Pose2D
+ ) -> Tuple[float, int]:
+ """Calculates minimal distance to global plan using kd-tree-search.
+
+ Args:
+ global_plan (np.array): Array containing 2D poses.
+ robot_pose (Pose2D): Robot position.
+
+ Returns:
+ Tuple[float, int]: Distance to the closes pose and index of the closes pose.
+ """
+ if self.kdtree is None:
+ self.kdtree = scipy.spatial.cKDTree(global_plan)
+
+ dist, index = self.kdtree.query([robot_pose.x, robot_pose.y])
+ return dist, index
+
+ def _reward_abrupt_direction_change(self, action: np.array = None):
+ """Applies a penalty when an abrupt change of direction occured.
+
+ Args:
+ action (np.array, optional): Array of shape (2,). First entry, linear velocity. \
+ Second entry, angular velocity. Defaults to None.
+ """
+ if self.last_action is not None:
+ curr_ang_vel = action[1]
+ last_ang_vel = self.last_action[1]
+
+ vel_diff = abs(curr_ang_vel - last_ang_vel)
+ self.curr_reward -= (vel_diff ** 4) / 2500
+ self.last_action = action
diff --git a/arena_marl/marl_agent/utils/sb3agent_format_check.py b/arena_marl/marl_agent/utils/sb3agent_format_check.py
new file mode 100644
index 000000000..e4d688774
--- /dev/null
+++ b/arena_marl/marl_agent/utils/sb3agent_format_check.py
@@ -0,0 +1,39 @@
+from typing import Type
+
+from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
+from torch.nn.modules.module import Module
+
+from arena_navigation.arena_local_planner.learning_based.arena_local_planner_drl.rl_agent.model.base_agent import BaseAgent, PolicyType
+
+
+def check_format(cls: Type[BaseAgent]):
+ assert isinstance(cls.type, PolicyType), "Type has to be of type 'PolicyType'!"
+
+ if cls.features_extractor_class:
+ assert issubclass(
+ cls.features_extractor_class, BaseFeaturesExtractor
+ ), "Feature extractors have to derive from 'BaseFeaturesExtractor'!"
+
+ if cls.features_extractor_kwargs:
+ assert (
+ type(cls.features_extractor_kwargs) is dict
+ ), "Features extractor kwargs have to be of type 'dict'!"
+
+ if cls.net_arch:
+ assert (
+ type(cls.net_arch) is list
+ ), "Network architecture kwargs have to be of type 'list'!"
+ for entry in cls.net_arch:
+ assert (
+ type(entry) is dict or type(entry) is int
+ ), "Network architecture entries have to be of either type 'list' or 'dict'!"
+ if type(entry) is dict:
+ assert "pi" in entry or "vf" in entry, (
+ "net_arch dictionaries have to contain either 'pi' or 'vf'"
+ "for the respective network head!"
+ )
+
+ if cls.activation_fn:
+ assert issubclass(
+ cls.activation_fn, Module
+ ), "Activation functions have to be taken from torch!"
diff --git a/arena_marl/marl_agent/utils/supersuit_utils.py b/arena_marl/marl_agent/utils/supersuit_utils.py
new file mode 100644
index 000000000..e2290ccbf
--- /dev/null
+++ b/arena_marl/marl_agent/utils/supersuit_utils.py
@@ -0,0 +1,99 @@
+from functools import partial
+from typing import Callable
+
+import numpy as np
+import rospy
+
+# from stable_baselines3.common.vec_env import VecNormalize
+from supersuit.vector import ConcatVecEnv, MarkovVectorEnv
+
+
+class MarkovVectorEnv_patched(MarkovVectorEnv):
+ """Patched environment wrapper which creates the correct API for vector environments. Dones for dead agents are returned as True instead as False."""
+
+ def step(self, actions):
+ agent_set = set(self.par_env.agents)
+ act_dict = {
+ agent: actions[i]
+ for i, agent in enumerate(self.par_env.possible_agents)
+ if agent in agent_set
+ }
+ observations, rewards, dones, infos = self.par_env.step(act_dict)
+
+ # adds last observation to info where user can get it
+ if all(dones.values()):
+ for agent, obs in observations.items():
+ infos[agent]["terminal_observation"] = obs
+
+ rews = np.array(
+ [rewards.get(agent, 0) for agent in self.par_env.possible_agents],
+ dtype=np.float32,
+ )
+ # we changed the default value to true instead of false
+ dns = np.array(
+ [dones.get(agent, True) for agent in self.par_env.possible_agents],
+ dtype=np.uint8,
+ )
+ infs = [infos.get(agent, {}) for agent in self.par_env.possible_agents]
+
+ if all(dones.values()):
+ observations = self.reset()
+ else:
+ observations = self.concat_obs(observations)
+ assert (
+ self.black_death or self.par_env.agents == self.par_env.possible_agents
+ ), "MarkovVectorEnv does not support environments with varying numbers of active agents unless black_death is set to True"
+ return observations, rews, dns, infs
+
+
+def vec_env_create(
+ env_fn: Callable,
+ agent_list_fn: Callable,
+ num_robots: int,
+ task_mode: str,
+ num_cpus: int,
+ num_vec_envs: int,
+ PATHS: dict,
+ agent_list_kwargs: dict,
+ max_num_moves_per_eps: int,
+):
+ """Function which vectorizes a given environment function in multiple parallel environments.
+
+ Args:
+ env_fn (Callable): Function that initializes an environment with wrappers
+ agent_list_fn (Callable): Object containing the program arguments
+ robot_model (str): Name of the robot model
+ num_robots (int): Number of robots in the environment
+ task_mode (str): Navigation task mode
+ num_cpus (int): Maximal number of CPUs to use (Currently only process is used anyhow)
+ num_vec_envs (int): Number of parallel environments to spawn
+ PATHS (dict): Dictionary which holds hyperparameters for the experiment
+
+ Returns:
+ SB3VecEnvWrapper: Vectorized environments following the SB3 VecEnv API. Each each robot in an environment \
+ poses as an environment in the vector.
+ """
+ import supersuit.vector.sb3_vector_wrapper as sb3vw
+
+ env_list_fns = [
+ partial(
+ env_fn,
+ ns=f"sim_{i}",
+ num_agents=num_robots,
+ task_mode=task_mode,
+ agent_list_fn=agent_list_fn,
+ PATHS=PATHS,
+ agent_list_kwargs=agent_list_kwargs,
+ max_num_moves_per_eps=max_num_moves_per_eps,
+ )
+ for i in range(1, num_vec_envs + 1)
+ ]
+ env = env_list_fns[0]()
+ action_space = env.action_space
+ observation_space = env.observation_space
+ metadata = env.metadata
+
+ num_cpus = min(num_cpus, num_vec_envs)
+ rospy.init_node("train_env", disable_signals=False, anonymous=True)
+ vec_env = ConcatVecEnv(env_list_fns, observation_space, action_space)
+ return sb3vw.SB3VecEnvWrapper(vec_env)
diff --git a/arena_marl/marl_agent/utils/supersuit_utils_experimental.py b/arena_marl/marl_agent/utils/supersuit_utils_experimental.py
new file mode 100644
index 000000000..1ddc3302c
--- /dev/null
+++ b/arena_marl/marl_agent/utils/supersuit_utils_experimental.py
@@ -0,0 +1,81 @@
+class SharedArray_patched(SharedArray):
+ def __setstate__(self, state):
+ (self.shared_arr, self.dtype, self.shape) = state
+ self._set_np_arr()
+
+class ProcConcatVec_patched(ProcConcatVec):
+ def __init__(self, vec_env_constrs, observation_space, action_space, tot_num_envs, metadata):
+ self.observation_space = observation_space
+ self.action_space = action_space
+ self.num_envs = num_envs = tot_num_envs
+ self.metadata = metadata
+
+ self.shared_obs = SharedArray_patched((num_envs,) + self.observation_space.shape, dtype=self.observation_space.dtype)
+ act_space_wrap = SpaceWrapper(self.action_space)
+ self.shared_act = SharedArray_patched((num_envs,) + act_space_wrap.shape, dtype=act_space_wrap.dtype)
+ self.shared_rews = SharedArray_patched((num_envs,), dtype=np.float32)
+ self.shared_dones = SharedArray_patched((num_envs,), dtype=np.uint8)
+
+ pipes = []
+ procs = []
+ for constr in vec_env_constrs:
+ inpt, outpt = mp.Pipe()
+ constr = gym.vector.async_vector_env.CloudpickleWrapper(constr)
+ proc = mp.Process(
+ target=async_loop, args=(constr, inpt, outpt, self.shared_obs, self.shared_act, self.shared_rews, self.shared_dones)
+ )
+ proc.start()
+ outpt.close()
+ pipes.append(inpt)
+ procs.append(proc)
+
+ self.pipes = pipes
+ self.procs = procs
+
+ num_envs = 0
+ env_nums = self._receive_info()
+ idx_starts = []
+ for pipe, cnum_env in zip(self.pipes, env_nums):
+ cur_env_idx = num_envs
+ num_envs += cnum_env
+ pipe.send(cur_env_idx)
+ idx_starts.append(cur_env_idx)
+
+ assert num_envs == tot_num_envs, f"num_envs={num_envs} und tot_num_envs={tot_num_envs}"
+ self.idx_starts = idx_starts
+
+class call_wrap_patched:
+ def __init__(self, fn, data, i):
+ self.fn = fn
+ self.data = data
+ self.i = i
+
+ def __call__(self, *args):
+ rospy.init_node(f"train_env_{self.i}", disable_signals=False, anonymous=True)
+ return self.fn(self.data)
+
+def MakeCPUAsyncConstructor_patched(max_num_cpus, metadata):
+ if True:
+ rospy.init_node("train_env", disable_signals=False, anonymous=True)
+ return ConcatVecEnv
+ else:
+
+ def constructor(env_fn_list, obs_space, act_space):
+ num_fns = len(env_fn_list)
+ envs_per_cpu = (num_fns + max_num_cpus - 1) // max_num_cpus
+ alloced_num_cpus = (num_fns + envs_per_cpu - 1) // envs_per_cpu
+
+ env_cpu_div = []
+ num_envs_alloced = 0
+ while num_envs_alloced < num_fns:
+ start_idx = num_envs_alloced
+ end_idx = min(num_fns, start_idx + envs_per_cpu)
+ env_cpu_div.append(env_fn_list[start_idx:end_idx])
+ num_envs_alloced = end_idx
+
+ assert alloced_num_cpus == len(env_cpu_div)
+
+ cat_env_fns = [call_wrap_patched(ConcatVecEnv, env_fns, i) for i, env_fns in enumerate(env_cpu_div)]
+ return ProcConcatVec_patched(cat_env_fns, obs_space, act_space, num_fns, metadata)
+
+ return constructor
\ No newline at end of file
diff --git a/arena_marl/marl_agent/utils/utils.py b/arena_marl/marl_agent/utils/utils.py
new file mode 100644
index 000000000..0741a5cea
--- /dev/null
+++ b/arena_marl/marl_agent/utils/utils.py
@@ -0,0 +1,101 @@
+import sys
+import os, sys, rospy, time
+
+from typing import Callable, List
+
+import rospy
+import rospkg
+
+from rl_agent.training_agent_wrapper import TrainingDRLAgent
+from rl_agent.base_agent_wrapper import BaseDRLAgent
+
+
+def get_hyperparameter_file(filename: str) -> str:
+ """Function which returns the path to the hyperparameter file.
+
+ Args:
+ filename (str): Name of the hyperparameter file.
+
+ Returns:
+ str: Path to the hyperparameter file.
+ """
+ return os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "hyperparameters",
+ filename,
+ )
+
+
+def instantiate_train_drl_agents(
+ num_robots: int = 1,
+ existing_robots: int = 0,
+ robot_model: str = "burger",
+ ns: str = None,
+ robot_name_prefix: str = "robot",
+ hyperparameter_path: str = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "hyperparameters",
+ "default.json",
+ ),
+) -> List[TrainingDRLAgent]:
+ """Function which generates a list agents which handle the ROS connection.
+
+ Args:
+ num_robots (int, optional): Number of robots in the environment. Defaults to 1.
+ robot_model (str, optional): Model of the robot. Defaults to "burger".
+ ns (str, optional): Name of the namespace (used for ROS topics). Defaults to None.
+ robot_name_prefix (str, optional): Name with which to prefix robots in the ROS environment. Defaults to "robot".
+ hyperparameter_path (str, optional): Path where to load hyperparameters from. Defaults to DEFAULT_HYPERPARAMETER.
+ action_space_path (str, optional): Path where to load action spaces from. Defaults to DEFAULT_ACTION_SPACE.
+
+ Returns:
+ List[TrainingDRLAgent]: List containing _num\_robots_ agent classes.
+ """
+ return [
+ TrainingDRLAgent(
+ ns=ns,
+ robot_model=robot_model,
+ robot_ns=robot_name_prefix + str(i + 1),
+ hyperparameter_path=hyperparameter_path,
+ )
+ for i in range(existing_robots, existing_robots + num_robots)
+ ]
+
+
+def instantiate_deploy_drl_agents(
+ num_robots: int = 1,
+ existing_robots: int = 0,
+ robot_model: str = "burger",
+ ns: str = None,
+ robot_name_prefix: str = "robot",
+ hyperparameter_path: str = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "hyperparameters",
+ "default.json",
+ ),
+) -> List[BaseDRLAgent]:
+ """Function which generates a list agents which handle the ROS connection.
+
+ Args:
+ num_robots (int, optional): Number of robots in the environment. Defaults to 1.
+ robot_model (str, optional): Model of the robot. Defaults to "burger".
+ ns (str, optional): Name of the namespace (used for ROS topics). Defaults to None.
+ robot_name_prefix (str, optional): Name with which to prefix robots in the ROS environment. Defaults to "robot".
+ hyperparameter_path (str, optional): Path where to load hyperparameters from. Defaults to DEFAULT_HYPERPARAMETER.
+ action_space_path (str, optional): Path where to load action spaces from. Defaults to DEFAULT_ACTION_SPACE.
+
+ Returns:
+ List[TrainingDRLAgent]: List containing _num\_robots_ agent classes.
+ """
+ return [
+ BaseDRLAgent(
+ ns=ns,
+ robot_model=robot_model,
+ robot_ns=robot_name_prefix + str(i + 1),
+ hyperparameter_path=hyperparameter_path,
+ )
+ for i in range(existing_robots, existing_robots + num_robots)
+ ]
diff --git a/arena_marl/marl_tools/__init__.py b/arena_marl/marl_tools/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/arena_marl/marl_tools/argsparser.py b/arena_marl/marl_tools/argsparser.py
new file mode 100644
index 000000000..6d7b3bbf2
--- /dev/null
+++ b/arena_marl/marl_tools/argsparser.py
@@ -0,0 +1,211 @@
+import argparse
+import os
+import numpy as np
+
+from rl_agent.model.agent_factory import AgentFactory
+from marl_tools.custom_mlp_utils import get_net_arch
+
+
+def training_args(parser):
+ """program arguments training script"""
+ parser.add_argument(
+ "--n_envs", type=int, default=1, help="number of parallel environments"
+ )
+ parser.add_argument(
+ "--no-gpu", action="store_true", help="disables gpu for training"
+ )
+ parser.add_argument(
+ "--debug",
+ action="store_true",
+ help="disables multiprocessing in order to debug",
+ )
+ group = parser.add_mutually_exclusive_group(required=True)
+ group.add_argument(
+ "--agent",
+ type=str,
+ choices=AgentFactory.registry.keys(),
+ help="predefined agent to train",
+ )
+ group.add_argument(
+ "--custom-mlp",
+ action="store_true",
+ help="enables training with custom multilayer perceptron",
+ )
+ group.add_argument(
+ "--load",
+ type=str,
+ metavar="[agent name]",
+ help="agent to be loaded for training",
+ )
+ parser.add_argument(
+ "--config",
+ type=str,
+ metavar="[config name]",
+ default="default",
+ help="name of the json file containing" "the hyperparameters",
+ )
+ parser.add_argument(
+ "--n", type=int, help="timesteps in total to be generated for training"
+ )
+ parser.add_argument(
+ "-log",
+ "--eval_log",
+ action="store_true",
+ help="enables storage of evaluation data",
+ )
+ parser.add_argument("--tb", action="store_true", help="enables tensorboard logging")
+
+
+def marl_training_args(parser):
+ parser.add_argument("--robots", type=int, default=1, help="number of robots")
+
+
+def run_agent_args(parser):
+ parser.add_argument(
+ "--no-gpu", action="store_true", help="disables gpu for training"
+ )
+ parser.add_argument(
+ "--load",
+ type=str,
+ metavar="[agent name]",
+ help="agent to be loaded for training",
+ )
+ parser.add_argument(
+ "--log",
+ action="store_true",
+ help="store log file with episode information",
+ )
+ parser.add_argument(
+ "-s",
+ "--scenario",
+ type=str,
+ metavar="[scenario name]",
+ default="scenario1",
+ help="name of scenario file for deployment",
+ )
+ parser.add_argument(
+ "--num_eps",
+ type=int,
+ metavar="[num episodes]",
+ default=100,
+ help="number of episodes the agent/s get/s challenged",
+ )
+ parser.add_argument(
+ "--max_steps",
+ type=int,
+ metavar="[max steps per episode]",
+ default=np.inf,
+ help="max amount of actions per episode before the simulation is resetted",
+ )
+ parser.add_argument("-v", "--verbose", choices=["0", "1"], default="1")
+
+
+def custom_mlp_args(parser):
+ """arguments for the custom mlp mode"""
+ custom_mlp_args = parser.add_argument_group(
+ "custom mlp args", "architecture arguments for the custom mlp"
+ )
+
+ custom_mlp_args.add_argument(
+ "--body",
+ type=str,
+ default="",
+ metavar="{num}-{num}-...",
+ help="architecture of the shared latent network, "
+ "each number representing the number of neurons per layer",
+ )
+ custom_mlp_args.add_argument(
+ "--pi",
+ type=str,
+ default="",
+ metavar="{num}-{num}-...",
+ help="architecture of the latent policy network, "
+ "each number representing the number of neurons per layer",
+ )
+ custom_mlp_args.add_argument(
+ "--vf",
+ type=str,
+ default="",
+ metavar="{num}-{num}-...",
+ help="architecture of the latent value network, "
+ "each number representing the number of neurons per layer",
+ )
+ custom_mlp_args.add_argument(
+ "--act_fn",
+ type=str,
+ default="relu",
+ choices=["relu", "sigmoid", "tanh"],
+ help="activation function to be applied after each hidden layer",
+ )
+
+
+def process_training_args(parsed_args):
+ """argument check function"""
+ if parsed_args.no_gpu:
+ os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+ if parsed_args.custom_mlp:
+ setattr(parsed_args, "net_arch", get_net_arch(parsed_args))
+ else:
+ if parsed_args.body != "" or parsed_args.pi != "" or parsed_args.vf != "":
+ print("[custom mlp] arguments will be ignored..")
+ delattr(parsed_args, "body")
+ delattr(parsed_args, "pi")
+ delattr(parsed_args, "vf")
+ delattr(parsed_args, "act_fn")
+
+
+def process_run_agent_args(parsed_args):
+ if parsed_args.no_gpu:
+ os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+
+def parse_training_args(args=None, ignore_unknown=False):
+ """parser for training script"""
+ arg_populate_funcs = [training_args, custom_mlp_args]
+ arg_check_funcs = [process_training_args]
+
+ return parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown)
+
+
+def parse_marl_training_args(args=None, ignore_unknown=False):
+ """parser for training script"""
+ arg_populate_funcs = [training_args, custom_mlp_args, marl_training_args]
+ arg_check_funcs = [process_training_args]
+
+ return parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown)
+
+
+def parse_run_agent_args(args=None, ignore_unknown=False):
+ """parser for training script"""
+ arg_populate_funcs = [run_agent_args]
+ arg_check_funcs = [process_run_agent_args]
+
+ return parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown)
+
+
+def parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown):
+ """generic arg parsing function"""
+ parser = argparse.ArgumentParser()
+
+ for func in arg_populate_funcs:
+ func(parser)
+
+ if ignore_unknown:
+ parsed_args, unknown_args = parser.parse_known_args(args=args)
+ else:
+ parsed_args = parser.parse_args(args=args)
+ unknown_args = []
+
+ for func in arg_check_funcs:
+ func(parsed_args)
+
+ print_args(parsed_args)
+ return parsed_args, unknown_args
+
+
+def print_args(args):
+ print("\n-------------------------------")
+ print(" ARGUMENTS ")
+ for k in args.__dict__:
+ print("- {} : {}".format(k, args.__dict__[k]))
+ print("--------------------------------\n")
diff --git a/arena_marl/marl_tools/custom_mlp_utils.py b/arena_marl/marl_tools/custom_mlp_utils.py
new file mode 100644
index 000000000..4ae5d200e
--- /dev/null
+++ b/arena_marl/marl_tools/custom_mlp_utils.py
@@ -0,0 +1,53 @@
+import torch as th
+import argparse
+
+
+def get_net_arch(args: argparse.Namespace):
+ """function to convert input args into valid syntax for the PPO"""
+ body, policy, value = None, None, None
+
+ if args.body != "":
+ body = parse_string(args.body)
+ if args.pi != "":
+ policy = parse_string(args.pi)
+ if args.vf != "":
+ value = parse_string(args.vf)
+
+ if body is None:
+ body = []
+ vf_pi = {}
+ if value is not None:
+ vf_pi["vf"] = value
+ if policy is not None:
+ vf_pi["pi"] = policy
+
+ return body + [vf_pi]
+
+
+def parse_string(string: str):
+ """function to convert a string into a int list
+
+ Example:
+
+ Input: parse_string("64-64")
+ Output: [64, 64]
+
+ """
+ string_arr = string.split("-")
+ int_list = []
+ for string in string_arr:
+ try:
+ int_list.append(int(string))
+ except:
+ raise Exception("Invalid argument format on: " + string)
+ return int_list
+
+
+def get_act_fn(act_fn_string: str):
+ """function to convert str into pytorch activation function class"""
+ if act_fn_string == "relu":
+ return th.nn.ReLU
+ elif act_fn_string == "sigmoid":
+ return th.nn.Sigmoid
+ elif act_fn_string == "tanh":
+ return th.nn.Tanh
diff --git a/arena_marl/marl_tools/staged_train_callback.py b/arena_marl/marl_tools/staged_train_callback.py
new file mode 100644
index 000000000..b9c5c5147
--- /dev/null
+++ b/arena_marl/marl_tools/staged_train_callback.py
@@ -0,0 +1,140 @@
+import warnings
+import rospy
+import numpy as np
+import time
+
+from typing import List
+from std_msgs.msg import Bool
+from stable_baselines3.common.callbacks import BaseCallback, EvalCallback, MarlEvalCallback
+from task_generator.task_generator.tasks import StagedRandomTask
+
+
+class InitiateNewTrainStage(BaseCallback):
+ """
+ Introduces new training stage when threshhold reached.
+ It must be used with "EvalCallback".
+
+ :param treshhold_type (str): checks threshhold for either percentage of successful episodes (succ) or mean reward (rew)
+ :param rew_threshold (int): mean reward threshold to trigger new stage
+ :param succ_rate_threshold (float): threshold percentage of succesful episodes to trigger new stage
+ :param task_mode (str): training task mode, if not 'staged' callback won't be called
+ :param verbose:
+ """
+
+ def __init__(
+ self,
+ n_envs: int = 1,
+ treshhold_type: str = "succ",
+ upper_threshold: float = 0,
+ lower_threshold: float = 0,
+ task_mode: str = "staged",
+ verbose=0,
+ ):
+
+ super(InitiateNewTrainStage, self).__init__(verbose=verbose)
+ self.n_envs = n_envs
+ self.threshhold_type = treshhold_type
+
+ assert self.threshhold_type in {
+ "rew",
+ "succ",
+ }, "given theshhold type neither 'rew' or 'succ'"
+
+ # default values
+ if self.threshhold_type == "rew" and upper_threshold == 0:
+ self.upper_threshold = 13
+ self.lower_threshold = 7
+ elif self.threshhold_type == "succ" and upper_threshold == 0:
+ self.upper_threshold = 0.85
+ self.lower_threshold = 0.6
+ else:
+ self.upper_threshold = upper_threshold
+ self.lower_threshold = lower_threshold
+
+ assert (
+ self.upper_threshold > self.lower_threshold
+ ), "upper threshold has to be bigger than lower threshold"
+ assert (
+ self.upper_threshold >= 0 and self.lower_threshold >= 0
+ ), "upper/lower threshold have to be positive numbers"
+ if self.threshhold_type == "succ":
+ assert (
+ self.upper_threshold <= 1 and self.lower_threshold >= 0
+ ), "succ thresholds have to be between [1.0, 0.0]"
+
+ self.verbose = verbose
+ self.activated = bool(task_mode == "staged")
+
+ if self.activated:
+ rospy.set_param("/last_stage_reached", False)
+ self._instantiate_publishers()
+
+ self._trigger = Bool()
+ self._trigger.data = True
+
+ def _instantiate_publishers(self):
+ self._publishers_next = []
+ self._publishers_previous = []
+
+ self._publishers_next.append(
+ rospy.Publisher(f"/eval_sim/next_stage", Bool, queue_size=1)
+ )
+ self._publishers_previous.append(
+ rospy.Publisher(f"/eval_sim/previous_stage", Bool, queue_size=1)
+ )
+
+ for env_num in range(self.n_envs):
+ self._publishers_next.append(
+ rospy.Publisher(f"/sim_{env_num+1}/next_stage", Bool, queue_size=1)
+ )
+ self._publishers_previous.append(
+ rospy.Publisher(f"/sim_{env_num+1}/previous_stage", Bool, queue_size=1)
+ )
+
+ def _on_step(self, EvalObject: EvalCallback) -> bool:
+ assert isinstance(
+ EvalObject, EvalCallback
+ ) or isinstance(
+ EvalObject, MarlEvalCallback
+ ), f"InitiateNewTrainStage must be called within EvalCallback"
+
+ if self.activated:
+ if EvalObject.n_eval_episodes < 20:
+ warnings.warn(
+ "Only %d evaluation episodes considered for threshold monitoring,"
+ "results might not represent agent performance well"
+ % EvalObject.n_eval_episodes
+ )
+
+ if (
+ self.threshhold_type == "rew"
+ and EvalObject.best_mean_reward <= self.lower_threshold
+ ) or (
+ self.threshhold_type == "succ"
+ and EvalObject.last_success_rate <= self.lower_threshold
+ ):
+ for i, pub in enumerate(self._publishers_previous):
+ pub.publish(self._trigger)
+ if i == 0:
+ self.log_curr_stage(EvalObject.logger)
+
+ if (
+ self.threshhold_type == "rew"
+ and EvalObject.best_mean_reward >= self.upper_threshold
+ ) or (
+ self.threshhold_type == "succ"
+ and EvalObject.last_success_rate >= self.upper_threshold
+ ):
+ if not rospy.get_param("/last_stage_reached"):
+ EvalObject.best_mean_reward = -np.inf
+ EvalObject.last_success_rate = -np.inf
+
+ for i, pub in enumerate(self._publishers_next):
+ pub.publish(self._trigger)
+ if i == 0:
+ self.log_curr_stage(EvalObject.logger)
+
+ def log_curr_stage(self, logger):
+ time.sleep(1)
+ curr_stage = rospy.get_param("/curr_stage", -1)
+ logger.record("train_stage/stage_idx", curr_stage)
diff --git a/arena_marl/marl_tools/train_agent_utils.py b/arena_marl/marl_tools/train_agent_utils.py
new file mode 100644
index 000000000..a0462f77c
--- /dev/null
+++ b/arena_marl/marl_tools/train_agent_utils.py
@@ -0,0 +1,565 @@
+from typing import Union, Type
+
+import argparse
+from datetime import datetime as dt
+import gym
+import json
+import os
+
+import yaml
+import rosnode
+import rospkg
+import time
+import warnings
+
+from stable_baselines3 import PPO
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.policies import ActorCriticPolicy
+from stable_baselines3.common.utils import set_random_seed
+
+
+from rl_agent.envs.flatland_gym_env import (
+ FlatlandEnv,
+)
+from rl_agent.model.agent_factory import AgentFactory
+from rl_agent.model.base_agent import BaseAgent
+from marl_tools.custom_mlp_utils import get_act_fn
+import rospy
+
+"""
+Dict containing agent specific hyperparameter keys (for documentation and typing validation purposes)
+
+:key agent_name: Precise agent name (as generated by get_agent_name())
+:key robot: Robot name to load robot specific .yaml file containing settings
+:key batch_size: Batch size (n_envs * n_steps)
+:key gamma: Discount factor
+:key n_steps: The number of steps to run for each environment per update
+:key ent_coef: Entropy coefficient for the loss calculation
+:key learning_rate: The learning rate, it can be a function
+ of the current progress remaining (from 1 to 0)
+ (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
+:key vf_coef: Value function coefficient for the loss calculation
+:key max_grad_norm: The maximum value for the gradient clipping
+:key gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
+:key m_batch_size: Minibatch size
+:key n_epochs: Number of epoch when optimizing the surrogate loss
+:key clip_range: Clipping parameter, it can be a function of the current progress
+ remaining (from 1 to 0).
+:key train_max_steps_per_episode: Max timesteps per training episode
+:key eval_max_steps_per_episode: Max timesteps per evaluation episode
+:key goal_radius: Radius of the goal
+:key reward_fnc: Number of the reward function (defined in ../rl_agent/utils/reward.py)
+:key discrete_action_space: If robot uses discrete action space
+:key normalize: If observations are normalized before fed to the network
+:key task_mode: Mode tasks will be generated in (custom, random, staged).
+:key curr_stage: In case of staged training which stage to start with.
+:param n_timesteps: The number of timesteps trained on in total.
+"""
+hyperparams = {
+ key: None
+ for key in [
+ "agent_name",
+ "robot",
+ "actions_in_observationspace",
+ "batch_size",
+ "gamma",
+ "n_steps",
+ "ent_coef",
+ "learning_rate",
+ "vf_coef",
+ "max_grad_norm",
+ "gae_lambda",
+ "m_batch_size",
+ "n_epochs",
+ "clip_range",
+ "reward_fnc",
+ "discrete_action_space",
+ "normalize",
+ "task_mode",
+ "curr_stage",
+ "train_max_steps_per_episode",
+ "eval_max_steps_per_episode",
+ "goal_radius",
+ ]
+}
+
+
+def load_config(config_name: str) -> dict:
+ """
+ Load config parameters from config file
+ """
+ config_location = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"), "configs", config_name
+ )
+ with open(config_location, "r", encoding="utf-8") as target:
+ config = yaml.load(target, Loader=yaml.FullLoader)
+
+ return config
+
+
+def initialize_hyperparameters(PATHS: dict, config: dict, n_envs: int) -> dict:
+ """
+ Write hyperparameters to json file in case agent is new otherwise load existing hyperparameters
+
+ :param PATHS: dictionary containing model specific paths
+ :param load_target: unique agent name (when calling --load)
+ :param config_name: name of the hyperparameter file in /configs/hyperparameters
+ :param n_envs: number of envs
+ """
+ # when building new agent
+ if config["resume"] is None:
+ hyperparams = load_hyperparameters_json(PATHS=PATHS, from_scratch=True)
+ hyperparams["agent_name"] = PATHS["model"].split("/")[-1]
+ else:
+ hyperparams = load_hyperparameters_json(PATHS=PATHS)
+
+ # dynamically adapt n_steps according to batch size and n envs
+ # then update .json
+ check_batch_size(n_envs, hyperparams["batch_size"], hyperparams["m_batch_size"])
+ hyperparams["n_steps"] = int(hyperparams["batch_size"] / n_envs)
+ if not rospy.get_param("debug_mode"):
+ write_hyperparameters_json(hyperparams, PATHS)
+ print_hyperparameters(hyperparams)
+ return hyperparams
+
+
+def write_hyperparameters_json(hyperparams: dict, PATHS: dict) -> None:
+ """
+ Write hyperparameters.json to agent directory
+
+ :param hyperparams: dict containing model specific hyperparameters
+ :param PATHS: dictionary containing model specific paths
+ """
+ doc_location = os.path.join(PATHS.get("model"), "hyperparameters.json")
+
+ with open(doc_location, "w", encoding="utf-8") as target:
+ json.dump(hyperparams, target, ensure_ascii=False, indent=4)
+
+
+def load_hyperparameters_json(PATHS: dict, from_scratch: bool = False) -> dict:
+ """
+ Load hyperparameters from model directory when loading - when training from scratch
+ load from ../configs/hyperparameters
+
+ :param PATHS: dictionary containing model specific paths
+ :param from_scatch: if training from scratch
+ :param config_name: file name of json file when training from scratch
+ """
+ if from_scratch:
+ doc_location = os.path.join(PATHS.get("hyperparams"))
+ else:
+ doc_location = os.path.join(PATHS.get("model"), "hyperparameters.json")
+
+ if os.path.isfile(doc_location):
+ with open(doc_location, "r") as file:
+ hyperparams = json.load(file)
+ check_hyperparam_format(loaded_hyperparams=hyperparams, PATHS=PATHS)
+ return hyperparams
+ else:
+ if from_scratch:
+ raise FileNotFoundError("Found no '%s'" % PATHS.get("hyperparams"))
+ else:
+ raise FileNotFoundError(
+ "Found no 'hyperparameters.json' in %s" % PATHS.get("model")
+ )
+
+
+def update_total_timesteps_json(timesteps: int, PATHS: dict) -> None:
+ """
+ Update total number of timesteps in json file
+
+ :param hyperparams_obj(object, agent_hyperparams): object containing containing model specific hyperparameters
+ :param PATHS: dictionary containing model specific paths
+ """
+ doc_location = os.path.join(PATHS.get("model"), "hyperparameters.json")
+ hyperparams = load_hyperparameters_json(PATHS=PATHS)
+
+ try:
+ curr_timesteps = int(hyperparams["n_timesteps"]) + timesteps
+ hyperparams["n_timesteps"] = curr_timesteps
+ except Exception:
+ raise Warning(
+ "Parameter 'total_timesteps' not found or not of type Integer in 'hyperparameter.json'!"
+ )
+ else:
+ with open(doc_location, "w", encoding="utf-8") as target:
+ json.dump(hyperparams, target, ensure_ascii=False, indent=4)
+
+
+def print_hyperparameters(hyperparams: dict) -> None:
+ print("\n--------------------------------")
+ print(" HYPERPARAMETERS \n")
+ for param, param_val in hyperparams.items():
+ print("{:30s}{:<10s}".format((param + ":"), str(param_val)))
+ print("--------------------------------\n\n")
+
+
+def check_hyperparam_format(loaded_hyperparams: dict, PATHS: dict) -> None:
+ if set(hyperparams.keys()) != set(loaded_hyperparams.keys()):
+ missing_keys = set(hyperparams.keys()).difference(
+ set(loaded_hyperparams.keys())
+ )
+ redundant_keys = set(loaded_hyperparams.keys()).difference(
+ set(hyperparams.keys())
+ )
+ raise AssertionError(
+ f"unmatching keys, following keys missing: {missing_keys} \n"
+ f"following keys unused: {redundant_keys}"
+ )
+ if not isinstance(loaded_hyperparams["discrete_action_space"], bool):
+ raise TypeError("Parameter 'discrete_action_space' not of type bool")
+ if loaded_hyperparams["task_mode"] not in ["custom", "random", "staged"]:
+ raise TypeError("Parameter 'task_mode' has unknown value")
+
+
+def update_hyperparam_model(
+ model: PPO, PATHS: dict, params: dict, n_envs: int = 1
+) -> None:
+ """
+ Updates parameter of loaded PPO agent when it was manually changed in the configs yaml.
+
+ :param model(object, PPO): loaded PPO agent
+ :param PATHS: program relevant paths
+ :param params: dictionary containing loaded hyperparams
+ :param n_envs: number of parallel environments
+ """
+ if model.batch_size != params["batch_size"]:
+ model.batch_size = params["batch_size"]
+ if model.gamma != params["gamma"]:
+ model.gamma = params["gamma"]
+ if model.n_steps != params["n_steps"]:
+ model.n_steps = params["n_steps"]
+ if model.ent_coef != params["ent_coef"]:
+ model.ent_coef = params["ent_coef"]
+ if model.learning_rate != params["learning_rate"]:
+ model.learning_rate = params["learning_rate"]
+ if model.vf_coef != params["vf_coef"]:
+ model.vf_coef = params["vf_coef"]
+ if model.max_grad_norm != params["max_grad_norm"]:
+ model.max_grad_norm = params["max_grad_norm"]
+ if model.gae_lambda != params["gae_lambda"]:
+ model.gae_lambda = params["gae_lambda"]
+ if model.n_epochs != params["n_epochs"]:
+ model.n_epochs = params["n_epochs"]
+ """
+ if model.clip_range != params['clip_range']:
+ model.clip_range = params['clip_range']
+ """
+ if model.n_envs != n_envs:
+ model.update_n_envs()
+ if model.rollout_buffer.buffer_size != params["n_steps"]:
+ model.rollout_buffer.buffer_size = params["n_steps"]
+ if model.tensorboard_log != PATHS["tb"]:
+ model.tensorboard_log = PATHS["tb"]
+
+
+def check_batch_size(n_envs: int, batch_size: int, mn_batch_size: int) -> None:
+ assert (
+ batch_size > mn_batch_size
+ ), f"Mini batch size {mn_batch_size} is bigger than batch size {batch_size}"
+
+ assert (
+ batch_size % mn_batch_size == 0
+ ), f"Batch size {batch_size} isn't divisible by mini batch size {mn_batch_size}"
+
+ assert (
+ batch_size % n_envs == 0
+ ), f"Batch size {batch_size} isn't divisible by n_envs {n_envs}"
+
+ assert (
+ batch_size % mn_batch_size == 0
+ ), f"Batch size {batch_size} isn't divisible by mini batch size {mn_batch_size}"
+
+
+def get_agent_name(args: argparse.Namespace) -> str:
+ """Function to get agent name to save to/load from file system
+
+ Example names:
+ "MLP_B_64-64_P_32-32_V_32-32_relu_2021_01_07__10_32"
+ "DRL_LOCAL_PLANNER_2021_01_08__7_14"
+
+ :param args (argparse.Namespace): Object containing the program arguments
+ """
+ START_TIME = dt.now().strftime("%Y_%m_%d__%H_%M")
+
+ if args.custom_mlp:
+ return (
+ "MLP_B_"
+ + args.body
+ + "_P_"
+ + args.pi
+ + "_V_"
+ + args.vf
+ + "_"
+ + args.act_fn
+ + "_"
+ + START_TIME
+ )
+ if args.load is None:
+ return args.agent + "_" + START_TIME
+ return args.load
+
+
+def get_MARL_agent_name_and_start_time() -> str:
+ """Function to get MARL agent parent dir, where seperate agents for different robots are saved"""
+ START_TIME = dt.now().strftime("%Y_%m_%d__%H_%M")
+
+ return "MARL_AGENTS_" + START_TIME, START_TIME
+
+
+def get_paths(
+ marl_dir: str,
+ robot: str,
+ agent_name: str,
+ config_params: dict,
+ curriculum: str,
+ eval_log: bool,
+ tb: bool,
+) -> dict:
+ """
+ Function to generate agent specific paths
+
+ :param agent_name: Precise agent name (as generated by get_agent_name())
+ :param args (argparse.Namespace): Object containing the program arguments
+ """
+ dir = rospkg.RosPack().get_path("arena_local_planner_drl")
+ PATHS = {
+ "model": os.path.join(dir, "agents", marl_dir, agent_name)
+ if config_params["resume"] is None
+ else config_params["resume"],
+ "tb": os.path.join(dir, "training_logs", "tensorboard", marl_dir, agent_name),
+ "eval": os.path.join(
+ dir, "training_logs", "train_eval_log", marl_dir, agent_name
+ ),
+ "robot_setting": os.path.join(
+ rospkg.RosPack().get_path("simulator_setup"),
+ "robot",
+ robot + ".model.yaml",
+ ),
+ "hyperparams": os.path.join(
+ dir, "configs", "hyperparameters", config_params["hyperparameter_file"]
+ ),
+ "robot_as": os.path.join(
+ dir, "configs", "action_spaces", "default_settings_" + robot + ".yaml"
+ ),
+ "curriculum": os.path.join(dir, "configs", "training_curriculums", curriculum),
+ }
+ rospy.get_param("/debug_mode")
+
+ # check for mode
+ if not rospy.get_param("/debug_mode"):
+ if config_params["resume"] is None:
+ os.makedirs(PATHS["model"])
+ elif not os.path.isfile(
+ os.path.join(PATHS["model"], agent_name + ".zip")
+ ) and not os.path.isfile(os.path.join(PATHS["model"], "best_model.zip")):
+ raise FileNotFoundError(
+ "Couldn't find model named %s.zip' or 'best_model.zip' in '%s'"
+ % (agent_name, PATHS["model"])
+ )
+
+ # evaluation log enabled
+ if eval_log:
+ if not os.path.exists(PATHS["eval"]):
+ os.makedirs(PATHS["eval"])
+ else:
+ PATHS["eval"] = None
+ # tensorboard log enabled
+ if tb:
+ if not os.path.exists(PATHS["tb"]):
+ os.makedirs(PATHS["tb"])
+ else:
+ PATHS["tb"] = None
+ else:
+ PATHS["eval"] = None
+ PATHS["tb"] = None
+
+ return PATHS
+
+
+def make_envs(
+ args: argparse.Namespace,
+ with_ns: bool,
+ rank: int,
+ params: dict,
+ seed: int = 0,
+ PATHS: dict = None,
+ train: bool = True,
+):
+ """
+ Utility function for multiprocessed env
+
+ :param with_ns: (bool) if the system was initialized with namespaces
+ :param rank: (int) index of the subprocess
+ :param params: (dict) hyperparameters of agent to be trained
+ :param seed: (int) the inital seed for RNG
+ :param PATHS: (dict) script relevant paths
+ :param train: (bool) to differentiate between train and eval env
+ :param args: (Namespace) program arguments
+ :return: (Callable)
+ """
+
+ def _init() -> Union[gym.Env, gym.Wrapper]:
+ train_ns = f"sim_{rank+1}" if with_ns else ""
+ eval_ns = f"eval_sim" if with_ns else ""
+
+ if train:
+ # train env
+ env = FlatlandEnv(
+ train_ns,
+ params["reward_fnc"],
+ params["discrete_action_space"],
+ goal_radius=params["goal_radius"],
+ max_steps_per_episode=params["train_max_steps_per_episode"],
+ debug=args.debug,
+ task_mode=params["task_mode"],
+ curr_stage=params["curr_stage"],
+ PATHS=PATHS,
+ )
+ else:
+ # eval env
+ env = Monitor(
+ FlatlandEnv(
+ eval_ns,
+ params["reward_fnc"],
+ params["discrete_action_space"],
+ goal_radius=params["goal_radius"],
+ max_steps_per_episode=params["eval_max_steps_per_episode"],
+ train_mode=False,
+ debug=args.debug,
+ task_mode=params["task_mode"],
+ curr_stage=params["curr_stage"],
+ PATHS=PATHS,
+ ),
+ PATHS.get("eval"),
+ info_keywords=("done_reason", "is_success"),
+ )
+ env.seed(seed + rank)
+ return env
+
+ set_random_seed(seed)
+ return _init
+
+
+def wait_for_nodes(
+ with_ns: bool, n_envs: int, timeout: int = 30, nodes_per_ns: int = 3
+) -> None:
+ """
+ Checks for timeout seconds if all nodes to corresponding namespace are online.
+
+ :param with_ns: (bool) if the system was initialized with namespaces
+ :param n_envs: (int) number of virtual environments
+ :param timeout: (int) seconds to wait for each ns
+ :param nodes_per_ns: (int) usual number of nodes per ns
+ """
+ if with_ns:
+ assert (
+ with_ns and n_envs >= 1
+ ), f"Illegal number of environments parsed: {n_envs}"
+ else:
+ assert (
+ not with_ns and n_envs == 1
+ ), f"Simulation setup isn't compatible with the given number of envs"
+
+ for i in range(n_envs):
+ for k in range(timeout):
+ ns = "sim_" + str(i + 1) if with_ns else ""
+ namespaces = rosnode.get_node_names(namespace=ns)
+
+ if len(namespaces) >= nodes_per_ns:
+ break
+
+ warnings.warn(
+ f"Check if all simulation parts of namespace '{ns}' are running properly"
+ )
+ warnings.warn(f"Trying to connect again..")
+ assert (
+ k < timeout - 1
+ ), f"Timeout while trying to connect to nodes of '{ns}'"
+
+ time.sleep(1)
+
+
+from stable_baselines3.common.vec_env import VecNormalize
+from stable_baselines3.common.vec_env.base_vec_env import VecEnv
+
+
+def load_vec_normalize(params: dict, PATHS: dict, env: VecEnv, eval_env: VecEnv):
+ if params["normalize"]:
+ load_path = os.path.join(PATHS["model"], "vec_normalize.pkl")
+ if os.path.isfile(load_path):
+ env = VecNormalize.load(load_path=load_path, venv=env)
+ eval_env = VecNormalize.load(load_path=load_path, venv=eval_env)
+ print("Succesfully loaded VecNormalize object from pickle file..")
+ else:
+ env = VecNormalize(
+ env,
+ training=True,
+ norm_obs=True,
+ norm_reward=False,
+ clip_reward=15,
+ )
+ eval_env = VecNormalize(
+ eval_env,
+ training=True,
+ norm_obs=True,
+ norm_reward=False,
+ clip_reward=15,
+ )
+ return env, eval_env
+
+
+def choose_agent_model(AGENT_NAME, PATHS, config, env, params, n_envs):
+ if config["resume"] is None:
+ agent: Union[
+ Type[BaseAgent], Type[ActorCriticPolicy]
+ ] = AgentFactory.instantiate(config["architecture_name"])
+ if isinstance(agent, BaseAgent):
+ model = PPO(
+ agent.type.value,
+ env,
+ policy_kwargs=agent.get_kwargs(),
+ gamma=params["gamma"],
+ n_steps=params["n_steps"],
+ ent_coef=params["ent_coef"],
+ learning_rate=params["learning_rate"],
+ vf_coef=params["vf_coef"],
+ max_grad_norm=params["max_grad_norm"],
+ gae_lambda=params["gae_lambda"],
+ batch_size=params["m_batch_size"],
+ n_epochs=params["n_epochs"],
+ clip_range=params["clip_range"],
+ tensorboard_log=PATHS.get("tb"),
+ verbose=1,
+ )
+ elif issubclass(agent, ActorCriticPolicy):
+ model = PPO(
+ agent,
+ env,
+ gamma=params["gamma"],
+ n_steps=params["n_steps"],
+ ent_coef=params["ent_coef"],
+ learning_rate=params["learning_rate"],
+ vf_coef=params["vf_coef"],
+ max_grad_norm=params["max_grad_norm"],
+ gae_lambda=params["gae_lambda"],
+ batch_size=params["m_batch_size"],
+ n_epochs=params["n_epochs"],
+ clip_range=params["clip_range"],
+ tensorboard_log=PATHS.get("tb"),
+ verbose=1,
+ )
+ else:
+ architecture_name = config["architecture_name"]
+ raise TypeError(
+ f"Registered agent class {architecture_name} is neither of type"
+ "'BaseAgent' or 'ActorCriticPolicy'!"
+ )
+ else:
+ # load flag
+ assert os.path.isfile(
+ os.path.join(config["resume"], "best_model.zip")
+ ), f"Couldn't find best model in {config['resume']}"
+ model = PPO.load(os.path.join(config["resume"], "best_model.zip"), env)
+ update_hyperparam_model(model, PATHS, params, n_envs)
+ return model
diff --git a/arena_marl/package.xml b/arena_marl/package.xml
new file mode 100755
index 000000000..8cf8131e7
--- /dev/null
+++ b/arena_marl/package.xml
@@ -0,0 +1,84 @@
+
+
+ arena_marl
+ 0.0.0
+ The arena_marl package
+
+
+
+
+ root
+
+
+
+
+
+ TODO
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ catkin
+ geometry_msgs
+ nav_msgs
+ roscpp
+ rospy
+ sensor_msgs
+ tf2
+ tf2_geometry_msgs
+ tf2_ros
+ geometry_msgs
+ nav_msgs
+ roscpp
+ rospy
+ sensor_msgs
+ tf2
+ tf2_geometry_msgs
+ tf2_ros
+ geometry_msgs
+ nav_msgs
+ roscpp
+ rospy
+ sensor_msgs
+ tf2
+ tf2_geometry_msgs
+ tf2_ros
+ flatland_msgs
+
+
+
+
+
+
+
+
diff --git a/arena_marl/scripts/__init__.py b/arena_marl/scripts/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/arena_marl/scripts/deployment/action_publisher.py b/arena_marl/scripts/deployment/action_publisher.py
new file mode 100755
index 000000000..cb74fc3d8
--- /dev/null
+++ b/arena_marl/scripts/deployment/action_publisher.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+import rospy
+import time
+
+from geometry_msgs.msg import Twist
+from rosgraph_msgs.msg import Clock
+from std_msgs.msg import Bool
+
+
+class ActionPublisher:
+ def __init__(self):
+ if rospy.get_param("train_mode"):
+ raise Exception("This node should be used solely in eval mode!")
+
+ rospy.init_node("action_publisher", anonymous=True)
+
+ self._step_size = rospy.get_param("step_size")
+ self._update_rate = rospy.get_param("update_rate")
+ # real time second in sim time
+ self._real_second_in_sim = self._step_size * self._update_rate
+ self._action_publish_rate = rospy.get_param("/robot_action_rate")
+
+ # apply rate in sim time
+ rate = (1 / self._action_publish_rate) / self._real_second_in_sim
+
+ ns_prefix = "" if "/single_env" in rospy.get_param_names() else "/eval_sim/"
+ self._pub_cmd_vel = rospy.Publisher(f"{ns_prefix}cmd_vel", Twist, queue_size=1)
+ self._pub_cycle_trigger = rospy.Publisher(
+ f"{ns_prefix}next_cycle", Bool, queue_size=1
+ )
+ self._sub = rospy.Subscriber(
+ f"{ns_prefix}cmd_vel_pub",
+ Twist,
+ self.callback_receive_cmd_vel,
+ queue_size=1,
+ )
+
+ # to measure sim time
+ # self._clock_sub = rospy.Subscriber(
+ # f"{ns_prefix}clock", Clock, self.callback_clock)
+ # last = 0
+
+ self._action = Twist()
+ self._signal = Bool()
+ self._clock = Clock().clock.to_sec()
+
+ last_action = self._action
+
+ while not rospy.is_shutdown():
+ if self._sub.get_num_connections() < 1:
+ print(f"ActionPublisher: No publisher to {ns_prefix}cmd_vel_pub yet.. ")
+ time.sleep(1)
+ continue
+
+ self._pub_cmd_vel.publish(self._action)
+ self._pub_cycle_trigger.publish(self._signal)
+
+ print(f"Published same action: {last_action==self._action}")
+ last_action = self._action
+
+ time.sleep(rate)
+
+ # print(f"sim time between cmd_vel: {self._clock - last}")
+ # last = self._clock
+
+ def callback_receive_cmd_vel(self, msg_cmd_vel: Twist):
+ self._action = msg_cmd_vel
+
+ def callback_clock(self, msg_clock: Clock):
+ self._clock = msg_clock.clock.to_sec()
+
+
+if __name__ == "__main__":
+ try:
+ ActionPublisher()
+ except rospy.ROSInterruptException:
+ pass
diff --git a/arena_marl/scripts/deployment/drl_agent_node.py b/arena_marl/scripts/deployment/drl_agent_node.py
new file mode 100755
index 000000000..f31c07efe
--- /dev/null
+++ b/arena_marl/scripts/deployment/drl_agent_node.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python
+import os
+import pickle
+import rospy
+import rospkg
+import sys
+
+from stable_baselines3 import PPO
+
+from flatland_msgs.srv import StepWorld, StepWorldRequest
+from rospy.exceptions import ROSException
+from std_msgs.msg import Bool
+
+from rl_agent.base_agent_wrapper import BaseDRLAgent
+
+
+""" TEMPORARY GLOBAL CONSTANTS """
+NS_PREFIX = ""
+TRAINED_MODELS_DIR = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"), "agents"
+)
+DEFAULT_ACTION_SPACE = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "default_settings.yaml",
+)
+
+
+class DeploymentDRLAgent(BaseDRLAgent):
+ def __init__(
+ self,
+ agent_name: str,
+ ns: str = None,
+ robot_name: str = None,
+ action_space_path: str = DEFAULT_ACTION_SPACE,
+ *args,
+ **kwargs,
+ ) -> None:
+ """Initialization procedure for the DRL agent node.
+
+ Args:
+ agent_name (str):
+ Agent name (directory has to be of the same name)
+ robot_name (str, optional):
+ Robot specific ROS namespace extension. Defaults to None.
+ ns (str, optional):
+ Simulation specific ROS namespace. Defaults to None.
+ action_space_path (str, optional):
+ Path to yaml file containing action space settings.
+ Defaults to DEFAULT_ACTION_SPACE.
+ """
+ self.name = agent_name
+ self.setup_agent()
+
+ hyperparameter_path = os.path.join(
+ TRAINED_MODELS_DIR, self.name, "hyperparameters.json"
+ )
+ super().__init__(
+ ns,
+ robot_name,
+ hyperparameter_path,
+ action_space_path,
+ )
+
+ if not self._is_train_mode:
+ rospy.init_node(f"DRL_local_planner", anonymous=True)
+
+ if self._is_train_mode:
+ # step world to fast forward simulation time
+ self._service_name_step = f"{self._ns}step_world"
+ self._sim_step_client = rospy.ServiceProxy(
+ self._service_name_step, StepWorld
+ )
+
+ def setup_agent(self) -> None:
+ """Loads the trained policy and when required the VecNormalize object."""
+ model_file = os.path.join(
+ TRAINED_MODELS_DIR, self.name, "best_model.zip"
+ )
+ vecnorm_file = os.path.join(
+ TRAINED_MODELS_DIR, self.name, "vec_normalize.pkl"
+ )
+
+ assert os.path.isfile(
+ model_file
+ ), f"Compressed model cannot be found at {model_file}!"
+ assert os.path.isfile(
+ vecnorm_file
+ ), f"VecNormalize file cannot be found at {vecnorm_file}!"
+
+ with open(vecnorm_file, "rb") as file_handler:
+ vec_normalize = pickle.load(file_handler)
+
+ custom_objects = {
+ "learning_rate": 0.0,
+ "lr_schedule": lambda _: 0.0,
+ "clip_range": lambda _: 0.0,
+ }
+
+ self._agent = PPO.load(model_file, custom_objects=custom_objects).policy
+ self._obs_norm_func = vec_normalize.normalize_obs
+
+ def run(self) -> None:
+ """Loop for running the agent until ROS is shutdown.
+
+ Note:
+ Calls the 'step_world'-service for fast-forwarding the \
+ simulation time in training mode. The simulation is forwarded \
+ by action_frequency seconds. Otherwise, communicates with \
+ the ActionPublisher node in order to comply with the specified \
+ action publishing rate.
+ """
+ while not rospy.is_shutdown():
+ if self._is_train_mode:
+ self.call_service_takeSimStep(self._action_frequency)
+ else:
+ self._wait_for_next_action_cycle()
+ obs = self.get_observations()[0]
+ action = self.get_action(obs)
+ self.publish_action(action)
+
+ def _wait_for_next_action_cycle(self) -> None:
+ """Stops the loop until a trigger message is sent by the ActionPublisher
+
+ Note:
+ Only use this method in combination with the ActionPublisher node!
+ That node is only booted when training mode is off.
+ """
+ try:
+ rospy.wait_for_message(f"{self._ns_robot}next_cycle", Bool)
+ except ROSException:
+ pass
+
+ def call_service_takeSimStep(self, t: float = None) -> None:
+ """Fast-forwards the simulation time.
+
+ Args:
+ t (float, optional):
+ Time in seconds. When t is None, time is forwarded by 'step_size' s.
+ Defaults to None.
+ """
+ request = StepWorldRequest() if t is None else StepWorldRequest(t)
+
+ try:
+ response = self._sim_step_client(request)
+ rospy.logdebug("step service=", response)
+ except rospy.ServiceException as e:
+ rospy.logdebug("step Service call failed: %s" % e)
+
+
+def main(agent_name: str) -> None:
+ AGENT = DeploymentDRLAgent(agent_name=agent_name, ns=NS_PREFIX)
+
+ try:
+ AGENT.run()
+ except rospy.ROSInterruptException:
+ pass
+
+
+if __name__ == "__main__":
+ AGENT_NAME = sys.argv[1]
+ main(agent_name=AGENT_NAME)
diff --git a/arena_marl/scripts/deployment/evaluation.py b/arena_marl/scripts/deployment/evaluation.py
new file mode 100644
index 000000000..aabf27a90
--- /dev/null
+++ b/arena_marl/scripts/deployment/evaluation.py
@@ -0,0 +1,127 @@
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+from stable_baselines3.common import base_class
+import supersuit.vector.sb3_vector_wrapper as sb3vw
+
+
+def evaluate_policy(
+ model: "base_class.BaseAlgorithm",
+ # env: Union[sb3vw.SB3VecEnvWrapper, VecEnv],
+ num_robots: int,
+ env: sb3vw.SB3VecEnvWrapper,
+ n_eval_episodes: int = 10,
+ deterministic: bool = True,
+ render: bool = False,
+ callback: Optional[Callable[[Dict[str, Any], Dict[str, Any]], None]] = None,
+ reward_threshold: Optional[float] = None,
+ return_episode_rewards: bool = False,
+ warn: bool = True,
+) -> Union[Tuple[float, float], Tuple[List[float], List[int]]]:
+ """
+ Runs policy for ``n_eval_episodes`` episodes and returns average reward.
+ This is made to work only with one env.
+
+ .. note::
+ If environment has not been wrapped with ``Monitor`` wrapper, reward and
+ episode lengths are counted as it appears with ``env.step`` calls. If
+ the environment contains wrappers that modify rewards or episode lengths
+ (e.g. reward scaling, early episode reset), these will affect the evaluation
+ results as well. You can avoid this by wrapping environment with ``Monitor``
+ wrapper before anything else.
+
+ :param model: The RL agent you want to evaluate.
+ :param env: The gym environment. In the case of a ``VecEnv``
+ this must contain only one environment.
+ :param n_eval_episodes: Number of episode to evaluate the agent
+ :param deterministic: Whether to use deterministic or stochastic actions
+ :param render: Whether to render the environment or not
+ :param callback: callback function to do additional checks,
+ called after each step. Gets locals() and globals() passed as parameters.
+ :param reward_threshold: Minimum expected reward per episode,
+ this will raise an error if the performance is not met
+ :param return_episode_rewards: If True, a list of rewards and episode lengths
+ per episode will be returned instead of the mean.
+ :param warn: If True (default), warns user about lack of a Monitor wrapper in the
+ evaluation environment.
+ :return: Mean reward per episode, std of reward per episode.
+ Returns ([float], [int]) when ``return_episode_rewards`` is True, first
+ list containing per-episode rewards and second containing per-episode lengths
+ (in number of steps).
+ """
+ is_monitor_wrapped = False
+ # Avoid circular import
+ from stable_baselines3.common.env_util import is_wrapped
+ from stable_baselines3.common.monitor import Monitor
+
+ not_reseted = True
+ # Avoid double reset, as VecEnv are reset automatically.
+ if not_reseted:
+ # Observations dictionary in {_agent name_: _respective observations_}
+ obs = env.reset()
+ not_reseted = False
+
+ agents = [a for a, _ in obs.items()]
+ episode_rewards = {a: [None] for a in agents}
+ episode_lengths = []
+
+ default_dones = {a: False for a in agents}
+ default_states = {a: None for a in agents}
+ default_actions = {a: None for a in agents}
+ default_episode_reward = {a: None for a in agents}
+ while len(episode_rewards) < n_eval_episodes:
+ # Number of loops here might differ from true episodes
+ # played, if underlying wrappers modify episode lengths.
+ # Avoid double reset, as VecEnv are reset automatically.
+ if not_reseted:
+ # Observations dictionary in {_agent name_: _respective observations_}
+ obs = env.reset()
+ not_reseted = False
+ dones = default_dones.copy()
+ states = default_states.copy()
+ actions = default_actions.copy()
+ episode_reward = default_episode_reward.copy()
+ episode_length = 0
+ while not dones:
+ # Get actions and states from each agent
+ for agent, state in states.items():
+ actions[agent], states[agent] = model.predict(
+ obs[agent], state, deterministic=deterministic
+ )
+
+ obs, rewards, dones, infos = env.step(actions)
+ for agent, reward in zip(agents, rewards.values()):
+ episode_reward[agent] += reward
+
+ if callback is not None:
+ callback(locals(), globals())
+
+ episode_length += 1
+ if render:
+ env.render()
+
+ # if is_monitor_wrapped:
+ # # Do not trust "done" with episode endings.
+ # # Remove vecenv stacking (if any)
+ # # if isinstance(env, VecEnv):
+ # # info = info[0]
+ # if "episode" in info.keys():
+ # # Monitor wrapper includes "episode" key in info if environment
+ # # has been wrapped with it. Use those rewards instead.
+ # episode_rewards.append(info["episode"]["r"])
+ # episode_lengths.append(info["episode"]["l"])
+ # else:
+ for agent, reward in episode_reward.items():
+ episode_rewards[agent].append(reward)
+ episode_lengths.append(episode_length)
+
+ mean_rewards = {agent: np.mean(episode_rewards[agent]) for agent in agents}
+ std_rewards = {agent: np.std(episode_rewards[agent]) for agent in agents}
+ if reward_threshold is not None:
+ assert min(mean_rewards.values()) > reward_threshold, (
+ "Atleast one mean reward below threshold: "
+ f"{min(mean_rewards.values()):.2f} < {reward_threshold:.2f}"
+ )
+ if return_episode_rewards:
+ return episode_rewards, episode_lengths
+ return mean_rewards, std_rewards
diff --git a/arena_marl/scripts/deployment/marl_deployment.py b/arena_marl/scripts/deployment/marl_deployment.py
new file mode 100644
index 000000000..ef1712d2b
--- /dev/null
+++ b/arena_marl/scripts/deployment/marl_deployment.py
@@ -0,0 +1,108 @@
+from multiprocessing import cpu_count
+import os
+
+
+from arena_marl.marl_agent.utils.utils import (
+ instantiate_deploy_drl_agents,
+ get_hyperparameter_file,
+ instantiate_train_drl_agents,
+)
+
+from arena_marl.scripts.deployment.evaluation import evaluate_policy
+
+from marl_tools.train_agent_utils import (
+ load_config,
+)
+from marl_tools import train_agent_utils
+
+import rospy
+
+from stable_baselines3.common.vec_env import DummyVecEnv
+from stable_baselines3 import PPO
+from arena_marl.marl_agent.utils.supersuit_utils import (
+ vec_env_create,
+)
+from arena_marl.marl_agent.envs.pettingzoo_env import env_fn
+
+
+from tools.argsparser import parse_training_args
+
+
+def main(args):
+ # load configuration
+ config = load_config(args.config)
+
+ # set debug_mode
+ rospy.set_param("debug_mode", config["debug_mode"])
+
+ # load agents
+ assert len(config["robots"]) == 1, "Only one robot type is supported"
+
+ robots, existing_robots = {}, 0
+
+ # loop over all robots in config
+ for robot_name, robot_train_params in config["robots"].items():
+ # Get the Base Agent instance(s)
+ robots[robot_name] = {
+ "agents": instantiate_train_drl_agents(
+ num_robots=robot_train_params["num_robots"],
+ robot_model=robot_name,
+ hyperparameter_path=get_hyperparameter_file(
+ robot_train_params["hyperparameter_file"]
+ ),
+ )
+ }
+ existing_robots += robot_train_params["num_robots"]
+
+ # Define paths. If the agent is a SARL agent, MARL_DIR is None.
+ MARL_DIR = ""
+ paths = train_agent_utils.get_paths(
+ MARL_DIR,
+ robot_name,
+ robot_train_params["resume"].split("/")[-1],
+ robot_train_params,
+ config["training_curriculum"]["training_curriculum_file"],
+ config["eval_log"],
+ config["tb"],
+ )
+
+ # Create env and store in dict
+ env = vec_env_create(
+ env_fn,
+ instantiate_train_drl_agents,
+ num_robots=robot_train_params["num_robots"],
+ num_cpus=cpu_count() - 1,
+ num_vec_envs=config["n_envs"],
+ task_mode=config["task_mode"],
+ PATHS=paths,
+ agent_list_kwargs={
+ "existing_robots": existing_robots,
+ "robot_model": robot_name,
+ },
+ max_num_moves_per_eps=config["max_num_moves_per_eps"],
+ )
+ robots[robot_name] = {"env": env}
+
+ # Load PPO model and store in dict
+ model = PPO.load(
+ os.path.join(robot_train_params["resume"], "best_model.zip"), env
+ )
+ robots[robot_name]["model"] = model
+
+ # Evaluate the policy for one robot type!
+ # Integration of multiple robot types is not yet implemented
+ mean_rewards, std_rewards = evaluate_policy(
+ model=robots[robot_name]["model"],
+ num_robots=robot_train_params["num_robots"],
+ env=robots[robot_name]["env"],
+ n_eval_episodes=config["periodic_eval"]["n_eval_episodes"],
+ return_episode_rewards=False,
+ )
+
+ print("Mean rewards: {}".format(mean_rewards))
+ print("Std rewards: {}".format(std_rewards))
+
+
+if __name__ == "__main__":
+ args, _ = parse_training_args()
+ main(args)
diff --git a/arena_marl/scripts/deployment/run_agent.py b/arena_marl/scripts/deployment/run_agent.py
new file mode 100644
index 000000000..6c758fc1a
--- /dev/null
+++ b/arena_marl/scripts/deployment/run_agent.py
@@ -0,0 +1,217 @@
+import os
+import sys
+import rospy
+import rospkg
+import json
+import numpy as np
+import time
+import warnings
+
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv, VecNormalize
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.evaluation import evaluate_policy
+
+from rl_agent.envs.flatland_gym_env import FlatlandEnv
+from task_generator.task_generator.tasks import StopReset
+from marl_tools.argsparser import parse_run_agent_args
+from marl_tools.train_agent_utils import (
+ load_hyperparameters_json,
+ print_hyperparameters,
+)
+
+### AGENT LIST ###
+AGENTS = [
+ "AGENT_1_2021_04_02__22_03",
+ "AGENT_2_2021_03_30__23_10",
+ "AGENT_3_2021_04_01__08_06",
+ "AGENT_4_2021_04_02__01_07",
+ "AGENT_5_2021_03_31__18_52",
+ "AGENT_6_2021_04_04__02_12",
+ "AGENT_7_2021_04_06__07_00",
+ "AGENT_8_2021_04_05__22_11",
+ "AGENT_9_2021_04_04__11_09",
+ "AGENT_10_2021_04_03__16_57",
+ "AGENT_11_2021_04_14__20_13",
+ "AGENT_12_2021_04_05__12_08",
+ "AGENT_13_2021_04_04__18_04",
+ "AGENT_14_2021_04_07__01_17",
+ "AGENT_15_2021_04_08__01_27",
+ "AGENT_16_2021_04_09__22_24",
+ "AGENT_17_2021_04_10__15_36",
+ "AGENT_18_2021_04_11__13_54",
+ "AGENT_19_2021_04_12__13_17",
+]
+
+
+def get_paths(args: dict, AGENT: str):
+ dir = rospkg.RosPack().get_path("arena_local_planner_drl")
+ PATHS = {
+ "model": os.path.join(dir, "agents", AGENT),
+ "vecnorm": os.path.join(dir, "agents", AGENT, "vec_normalize.pkl"),
+ "robot_setting": os.path.join(
+ rospkg.RosPack().get_path("simulator_setup"), "robot", "myrobot.model.yaml"
+ ),
+ "robot_as": os.path.join(dir, "configs", "default_settings.yaml"),
+ "scenario": os.path.join(
+ rospkg.RosPack().get_path("simulator_setup"),
+ "scenarios",
+ args.scenario + ".json",
+ ),
+ "curriculum": os.path.join(
+ dir, "configs", "training_curriculum_map1small.yaml"
+ ),
+ "log": os.path.join(dir, "evaluation_logs", AGENT),
+ }
+ if args.log and not os.path.exists(PATHS["log"]):
+ os.makedirs(PATHS["log"])
+ return PATHS
+
+
+def make_env(
+ with_ns: bool, PATHS: dict, PARAMS: dict, log: bool = False, max_steps: int = 1000
+):
+ """
+ Utility function for the evaluation environment.
+
+ :param params: (dict) hyperparameters of agent to be trained
+ :param PATHS: (dict) script relevant paths
+ :param log: (bool) to differentiate between train and eval env
+ :param max_steps: (int) number of steps before the episode is stopped
+ :return: (Callable)
+ """
+
+ def _init():
+ ns = f"eval_sim" if with_ns else ""
+
+ env = FlatlandEnv(
+ ns,
+ PARAMS["reward_fnc"],
+ PARAMS["discrete_action_space"],
+ goal_radius=0.05,
+ max_steps_per_episode=max_steps,
+ train_mode=False,
+ task_mode="scenario",
+ PATHS=PATHS,
+ curr_stage=4,
+ extended_eval=True,
+ )
+ if log:
+ # eval env
+ env = Monitor(
+ env,
+ PATHS["log"],
+ False,
+ info_keywords=(
+ "collisions",
+ "distance_travelled",
+ "time_safe_dist",
+ "time",
+ "done_reason",
+ "is_success",
+ ),
+ )
+ return env
+
+ return _init
+
+
+if __name__ == "__main__":
+ args, _ = parse_run_agent_args()
+
+ if args.load:
+ AGENTS = [args.load]
+ assert len(AGENTS) > 0, "No agent name was given for evaluation"
+
+ ros_params = rospy.get_param_names()
+ ns_for_nodes = "/single_env" not in ros_params
+
+ start = time.time()
+ while len(AGENTS) != 0:
+ AGENT = AGENTS.pop(0)
+ print(f"START RUNNING AGENT: {AGENT}")
+ PATHS = get_paths(args, AGENT)
+
+ assert os.path.isfile(os.path.join(PATHS["model"], "best_model.zip")), (
+ "No model file found in %s" % PATHS["model"]
+ )
+ assert os.path.isfile(PATHS["scenario"]), (
+ "No scenario file named %s" % PATHS["scenario"]
+ )
+
+ PARAMS = load_hyperparameters_json(PATHS)
+ print_hyperparameters(PARAMS)
+
+ env = DummyVecEnv(
+ [make_env(ns_for_nodes, PATHS, PARAMS, args.log, args.max_steps)]
+ )
+ if PARAMS["normalize"]:
+ if not os.path.isfile(PATHS["vecnorm"]):
+ # without it agent performance will be strongly altered
+ warnings.warn(
+ f"Couldn't find VecNormalize pickle for {PATHS['model'].split('/')[-1]}, going to skip this model"
+ )
+ continue
+
+ env = VecNormalize.load(PATHS["vecnorm"], env)
+
+ # load agent
+ agent = PPO.load(os.path.join(PATHS["model"], "best_model.zip"), env)
+
+ try:
+ evaluate_policy(
+ model=agent, env=env, n_eval_episodes=args.num_eps, deterministic=True
+ )
+ except StopReset:
+ pass
+
+ time = round(time.time() - start)
+ print(f"Time passed: {time}s")
+ print("EVALUATION DONE!")
+ sys.exit()
+
+ # env.reset()
+ # first_obs = True
+
+ # # iterate through each scenario max_repeat times
+ # while True:
+ # if first_obs:
+ # # send action 'stand still' in order to get first obs
+ # if params['discrete_action_space']:
+ # obs, rewards, dones, info = env.step([6])
+ # else:
+ # obs, rewards, dones, info = env.step([[0.0, 0.0]])
+ # first_obs = False
+ # cum_reward = 0.0
+
+ # # timer = time.time()
+ # action, _ = agent.predict(obs, deterministic=True)
+ # # print(f"Action predict time: {(time.time()-timer)*2.5} (sim time)")
+
+ # # clip action
+ # if not params['discrete_action_space']:
+ # action = np.maximum(
+ # np.minimum(agent.action_space.high, action), agent.action_space.low)
+
+ # # apply action
+ # obs, rewards, done, info = env.step(action)
+
+ # cum_reward += rewards
+
+ # if done:
+ # if args.verbose == '1':
+ # if info[0]['done_reason'] == 0:
+ # done_reason = "exceeded max steps"
+ # elif info[0]['done_reason'] == 1:
+ # done_reason = "collision"
+ # else:
+ # done_reason = "goal reached"
+
+ # print("Episode finished with reward of %f (finish reason: %s)"% (cum_reward, done_reason))
+ # env.reset()
+ # first_obs = True
+
+ # time.sleep(0.001)
+ # if rospy.is_shutdown():
+ # print('shutdown')
+ # break
diff --git a/arena_marl/scripts/training/marl_test_script.py b/arena_marl/scripts/training/marl_test_script.py
new file mode 100644
index 000000000..b9b79e37a
--- /dev/null
+++ b/arena_marl/scripts/training/marl_test_script.py
@@ -0,0 +1,125 @@
+import numpy as np
+import rospy
+import rospkg
+import os
+
+from stable_baselines3 import PPO
+from stable_baselines3.common.callbacks import MarlEvalCallback
+from supersuit import pettingzoo_env_to_vec_env_v0, black_death_v2
+from supersuit.vector.sb3_vector_wrapper import SB3VecEnvWrapper
+
+rospy.set_param("/MARL", True)
+rospy.set_param("/num_robots", 8)
+
+from rl_agent.training_agent_wrapper import TrainingDRLAgent
+from scripts.deployment.drl_agent_node import DeploymentDRLAgent
+from marl_agent.envs.pettingzoo_env import FlatlandPettingZooEnv
+
+from nav_msgs.srv import GetMap
+
+DEFAULT_HYPERPARAMETER = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "hyperparameters",
+ "default.json",
+)
+DEFAULT_ACTION_SPACE = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "default_settings.yaml",
+)
+
+
+def instantiate_drl_agents(
+ num_robots: int = 1,
+ ns: str = None,
+ robot_name_prefix: str = "robot",
+ hyperparameter_path: str = DEFAULT_HYPERPARAMETER,
+ action_space_path: str = DEFAULT_ACTION_SPACE,
+) -> list:
+ return [
+ TrainingDRLAgent(
+ ns=ns,
+ robot_name=robot_name_prefix + str(i + 1),
+ hyperparameter_path=hyperparameter_path,
+ action_space_path=action_space_path,
+ )
+ for i in range(num_robots)
+ ]
+
+
+def main():
+ # rospy.init_node(f"USER_NODE", anonymous=True)
+
+ # agent_list = instantiate_drl_agents(
+ # num_robots=8,
+ # ns="sim_1",
+ # robot_name_prefix=rospy.get_param("base_robot_name", default="robot"),
+ # )
+ NUM_ROBOTS = 8
+
+ env = SB3VecEnvWrapper(
+ pettingzoo_env_to_vec_env_v0(
+ black_death_v2(
+ FlatlandPettingZooEnv(
+ num_agents=NUM_ROBOTS,
+ ns="sim_1",
+ agent_list_fn=instantiate_drl_agents,
+ max_num_moves_per_eps=2000,
+ )
+ )
+ )
+ )
+ obs = env.reset()
+
+ # AGENT = DeploymentDRLAgent(
+ # agent_name="rule_04", ns="sim_1", robot_name="test1"
+ # )
+
+ model = PPO("MlpPolicy", env)
+ model.learn(
+ total_timesteps=200000,
+ callbacks=get_evalcallback(
+ num_robots=NUM_ROBOTS,
+ ),
+ reset_num_timesteps=True,
+ )
+ exit()
+ agent_names = env.agents
+ for _ in range(100000000):
+ if not agent_names:
+ agent_names = env.possible_agents[:]
+ obs = env.reset()
+
+ actions = {agent: AGENT.get_action(obs[agent]) for agent in agent_names}
+ obs, rewards, dones, infos = env.step(actions)
+
+ done_agents = [k for k, v in dones.items() if v]
+ for agent in done_agents:
+ agent_names.remove(agent)
+
+ if done_agents:
+ for agent in done_agents:
+ if infos[agent]["is_success"]:
+ print(f"{agent}: finito")
+
+
+def get_evalcallback(num_robots: int) -> MarlEvalCallback:
+ eval_env = FlatlandPettingZooEnv(
+ num_agents=num_robots,
+ ns="eval_sim",
+ agent_list_fn=instantiate_drl_agents,
+ max_num_moves_per_eps=2000,
+ )
+
+ return MarlEvalCallback(
+ eval_env,
+ num_robots,
+ n_eval_episodes=40,
+ eval_freq=1,
+ deterministic=True,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/arena_marl/scripts/training/train_marl_agent.py b/arena_marl/scripts/training/train_marl_agent.py
new file mode 100644
index 000000000..689bd45d1
--- /dev/null
+++ b/arena_marl/scripts/training/train_marl_agent.py
@@ -0,0 +1,226 @@
+import sys
+import os, sys, rospy, time
+
+from datetime import time
+from multiprocessing import cpu_count
+
+import rospy
+from multiprocessing import cpu_count, set_start_method
+
+# from stable_baselines3 import PPO
+# from supersuit.vector import MakeCPUAsyncConstructor, ConcatVecEnv
+# from supersuit.vector.sb3_vector_wrapper import SB3VecEnvWrapper
+
+# from marl_tools.custom_mlp_utils import *
+from arena_navigation.arena_local_planner.learning_based.arena_local_planner_drl.rl_agent.model.custom_policy import *
+from arena_navigation.arena_local_planner.learning_based.arena_local_planner_drl.rl_agent.model.custom_sb3_policy import *
+
+# from tools.argsparser import parse_training_args
+from marl_tools.staged_train_callback import InitiateNewTrainStage
+
+from tools.argsparser import parse_training_args
+
+from arena_marl.marl_agent.envs.pettingzoo_env import env_fn
+from arena_marl.marl_agent.utils.supersuit_utils import (
+ vec_env_create,
+)
+
+# from tools.argsparser import parse_marl_training_args
+from marl_tools.train_agent_utils import (
+ get_MARL_agent_name_and_start_time,
+ get_paths,
+ choose_agent_model,
+ load_config,
+ initialize_hyperparameters,
+)
+from marl_tools import train_agent_utils
+
+from stable_baselines3.common.callbacks import (
+ StopTrainingOnRewardThreshold,
+ MarlEvalCallback,
+)
+
+from arena_navigation.arena_local_planner.learning_based.arena_local_planner_drl.tools.train_agent_utils import *
+
+from marl_agent.utils.utils import instantiate_train_drl_agents
+
+
+def main(args):
+ # load configuration
+ config = load_config(args.config)
+
+ # set debug_mode
+ rospy.set_param("debug_mode", config["debug_mode"])
+
+ robots, existing_robots = {}, 0
+ MARL_NAME, START_TIME = get_MARL_agent_name_and_start_time()
+
+ # create seperate model instances for each robot
+ for robot_name, robot_train_params in config["robots"].items():
+ # generate agent name and model specific paths
+ agent_name = (
+ robot_train_params["resume"].split("/")[-1]
+ if robot_train_params["resume"]
+ else f"{robot_name}_{START_TIME}"
+ )
+
+ paths = train_agent_utils.get_paths(
+ MARL_NAME,
+ robot_name,
+ agent_name,
+ robot_train_params,
+ config["training_curriculum"]["training_curriculum_file"],
+ config["eval_log"],
+ config["tb"],
+ )
+
+ # initialize hyperparameters (save to/ load from json)
+ hyper_params = train_agent_utils.initialize_hyperparameters(
+ PATHS=paths,
+ config=robot_train_params,
+ n_envs=config["n_envs"],
+ )
+
+ env = vec_env_create(
+ env_fn,
+ instantiate_train_drl_agents,
+ num_robots=robot_train_params["num_robots"],
+ num_cpus=cpu_count() - 1,
+ num_vec_envs=config["n_envs"],
+ task_mode=config["task_mode"],
+ PATHS=paths,
+ agent_list_kwargs={
+ "existing_robots": existing_robots,
+ "robot_model": robot_name,
+ },
+ max_num_moves_per_eps=config["max_num_moves_per_eps"],
+ )
+
+ existing_robots += robot_train_params["num_robots"]
+
+ # env = VecNormalize(
+ # env,
+ # training=True,
+ # norm_obs=True,
+ # norm_reward=True,
+ # clip_reward=15,
+ # clip_obs=15,
+ # )
+
+ model = choose_agent_model(
+ agent_name, paths, robot_train_params, env, hyper_params, config["n_envs"]
+ )
+
+ # add configuration for one robot to robots dictionary
+ robots[robot_name] = {
+ "model": model,
+ "env": env,
+ "n_envs": config["n_envs"],
+ "robot_train_params": robot_train_params,
+ "hyper_params": hyper_params,
+ "paths": paths,
+ }
+
+ # set num of timesteps to be generated
+ n_timesteps = 40000000 if config["n_timesteps"] is None else config["n_timesteps"]
+
+ start = time.time()
+ try:
+ model = robots[robot_name]["model"]
+ model.learn(
+ total_timesteps=n_timesteps,
+ reset_num_timesteps=True,
+ # Übergib einfach das dict für den aktuellen roboter
+ callback=get_evalcallback(
+ eval_config=config["periodic_eval"],
+ curriculum_config=config["training_curriculum"],
+ stop_training_config=config["stop_training"],
+ train_env=robots[robot_name]["env"],
+ num_robots=robots[robot_name]["robot_train_params"]["num_robots"],
+ num_envs=config["n_envs"],
+ task_mode=config["task_mode"],
+ PATHS=robots[robot_name]["paths"],
+ ),
+ )
+ except KeyboardInterrupt:
+ print("KeyboardInterrupt..")
+ # finally:
+ # update the timesteps the model has trained in total
+ # update_total_timesteps_json(n_timesteps, PATHS)
+
+ robots[robot_name][model].env.close()
+ print(f"Time passed: {time.time() - start}s")
+ print("Training script will be terminated")
+ sys.exit()
+
+
+def get_evalcallback(
+ eval_config: dict,
+ curriculum_config: dict,
+ stop_training_config: dict,
+ train_env: VecEnv,
+ num_robots: int,
+ num_envs: int,
+ task_mode: str,
+ PATHS: dict,
+) -> MarlEvalCallback:
+ """Function which generates an evaluation callback with an evaluation environment.
+
+ Args:
+ train_env (VecEnv): Vectorized training environment
+ num_robots (int): Number of robots in the environment
+ num_envs (int): Number of parallel spawned environments
+ task_mode (str): Task mode for the current experiment
+ PATHS (dict): Dictionary which holds hyperparameters for the experiment
+
+ Returns:
+ MarlEvalCallback: [description]
+ """
+ eval_env = env_fn(
+ num_agents=num_robots,
+ ns="eval_sim",
+ agent_list_fn=instantiate_train_drl_agents,
+ max_num_moves_per_eps=eval_config["max_num_moves_per_eps"],
+ PATHS=PATHS,
+ )
+
+ # eval_env = VecNormalize(
+ # eval_env,
+ # training=False,
+ # norm_obs=True,
+ # norm_reward=False,
+ # clip_reward=15,
+ # clip_obs=3.5,
+ # )
+
+ return MarlEvalCallback(
+ train_env=train_env,
+ eval_env=eval_env,
+ num_robots=num_robots,
+ n_eval_episodes=eval_config["n_eval_episodes"],
+ eval_freq=eval_config["eval_freq"],
+ deterministic=True,
+ log_path=PATHS["eval"],
+ best_model_save_path=PATHS["model"],
+ callback_on_eval_end=InitiateNewTrainStage(
+ n_envs=num_envs,
+ treshhold_type=curriculum_config["threshold_type"],
+ upper_threshold=curriculum_config["upper_threshold"],
+ lower_threshold=curriculum_config["lower_threshold"],
+ task_mode=task_mode,
+ verbose=1,
+ ),
+ callback_on_new_best=StopTrainingOnRewardThreshold(
+ treshhold_type=stop_training_config["threshold_type"],
+ threshold=stop_training_config["threshold"],
+ verbose=1,
+ ),
+ )
+
+
+if __name__ == "__main__":
+ set_start_method("fork")
+ # args, _ = parse_marl_training_args()
+ args, _ = parse_training_args()
+ # rospy.init_node("train_env", disable_signals=False, anonymous=True)
+ main(args)
diff --git a/arena_marl/setup.py b/arena_marl/setup.py
new file mode 100755
index 000000000..2dd27cb99
--- /dev/null
+++ b/arena_marl/setup.py
@@ -0,0 +1,16 @@
+## ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD
+
+from distutils.core import setup
+from catkin_pkg.python_setup import generate_distutils_setup
+
+# fetch values from package.xml
+setup_args = generate_distutils_setup(
+ packages=[
+ "rl_agent",
+ "rl_agent.envs",
+ "rl_agent.utils",
+ "scripts",
+ "tools",
+ ],
+)
+setup(**setup_args)
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings.yaml
similarity index 96%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings.yaml
index cc4c53e98..bcf07f4f1 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings.yaml
@@ -1,4 +1,5 @@
robot:
+ holonomic: False
discrete_actions:
- name: move_forward
linear: 0.30
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_agvota.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_agvota.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_agvota.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_agvota.yaml
index de7680846..d9c3a3482 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_agvota.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_agvota.yaml
@@ -1,4 +1,5 @@
robot:
+ radius: 0.629
holonomic: False
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_burger.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_burger.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_burger.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_burger.yaml
index 749d30395..dc0782237 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_burger.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_burger.yaml
@@ -1,4 +1,5 @@
robot:
+ radius: 0.113
holonomic: False
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_cob4.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_cob4.yaml
similarity index 98%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_cob4.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_cob4.yaml
index 2fb2c227b..41ed40c74 100644
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_cob4.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_cob4.yaml
@@ -1,5 +1,6 @@
# TODO: The discrete actions have not been changed (from ridgeback here)
robot:
+ radius: 0.36
holonomic: True
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_jackal.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_jackal.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_jackal.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_jackal.yaml
index 7d43b0e1d..2e6ddbed4 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_jackal.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_jackal.yaml
@@ -1,4 +1,5 @@
robot:
+ radius: 0.267
holonomic: False
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_ridgeback.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_ridgeback.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_ridgeback.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_ridgeback.yaml
index 10cbb2319..eeae900c3 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_ridgeback.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_ridgeback.yaml
@@ -1,4 +1,5 @@
robot:
+ radius: 0.625
holonomic: True
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_rto.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_rto.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_rto.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_rto.yaml
index 1157ed13c..0ccfef053 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_rto.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_rto.yaml
@@ -1,5 +1,6 @@
# TODO: The discrete actions have not been changed (from ridgeback here)
robot:
+ radius: 0.225
holonomic: True
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_rto_real.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_rto_real.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_rto_real.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_rto_real.yaml
index 8e9005ef2..b9fee66ea 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_rto_real.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_rto_real.yaml
@@ -1,5 +1,6 @@
# TODO: The discrete actions have not been changed (from ridgeback here)
robot:
+ radius: 0.225
holonomic: True
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_tiago.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_tiago.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_tiago.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_tiago.yaml
index 78232be97..c7a2d634c 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_tiago.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_tiago.yaml
@@ -1,5 +1,6 @@
# TODO: @Tuan adjust the discrete actions
robot:
+ radius: 0.27
holonomic: False
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_waffle_pi.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_waffle_pi.yaml
similarity index 97%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_waffle_pi.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_waffle_pi.yaml
index 3fd4756f6..1cdd02a0c 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_waffle_pi.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_waffle_pi.yaml
@@ -1,5 +1,6 @@
# TODO: The discrete actions have not been changed (from burger here)
robot:
+ radius: 0.208
holonomic: False
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_youbot.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_youbot.yaml
similarity index 98%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_youbot.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_youbot.yaml
index 40ef0e741..bfe85d23f 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/default_settings_youbot.yaml
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/action_spaces/default_settings_youbot.yaml
@@ -2,6 +2,7 @@
# values from: https://github.com/youbot/youbot_navigation/blob/hydro-devel/youbot_navigation_common/config/base_local_planner_params.yaml
# and values from here: http://www.youbot-store.com/wiki/index.php/YouBot_Detailed_Specifications
robot:
+ radius: 0.347
holonomic: True
discrete_actions:
- name: move_forward
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/deployment_config.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/deployment_config.yaml
new file mode 100644
index 000000000..f5503fb10
--- /dev/null
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/deployment_config.yaml
@@ -0,0 +1,68 @@
+# in debug_mode no agent directories will be created and no models will be saved
+debug_mode: true
+
+# Hardware specifc configurations
+# number of parallel environments
+n_envs: 1
+# gpu yes or no
+no_gpu: false
+
+
+### Global configuration that applies to all robots
+# navigation task mode, chose from "random" or "staged"
+task_mode: "staged"
+# number of simulation timesteps
+n_timesteps: 40000000
+max_num_moves_per_eps: 1000
+
+periodic_eval:
+ # max number of steps per episode
+ max_num_moves_per_eps: 1000
+ # number of evaluation episodes
+ n_eval_episodes: 100
+ # evaluation frequency, evaluation after every n_envs * 20000 timesteps
+ eval_freq: 20000
+
+### training_curriculum
+# threshold metric to be considered during evaluation
+# can be either "succ" (success rate) or "rew" (reward)
+training_curriculum:
+ # file for the robot's learning curriculum
+ training_curriculum_file: "training_curriculum.yaml"
+ threshold_type: "succ"
+ upper_threshold: 0.8
+ lower_threshold: 0.6
+
+### stop training on threshold
+# stops training when last stage reached and threshold satisfied
+stop_training:
+ threshold_type: "succ"
+ threshold: 0.9
+
+
+# save evaluation stats during training in log file
+eval_log: false
+# use tensorboard
+tb: false
+
+robots:
+ jackal:
+ # number of robots of this type
+ num_robots: 2
+ # name of hyperparameter file located in the configs/hyperparameters directory
+ hyperparameter_file: "default.json"
+ # name of architecture defined in the Policy factory
+ architecture_name: "AGENT_24"
+ # path to latest checkpoint; if provided the training will be resumed from that checkpoint
+ resume: "/home/ignc/catkin_ws/src/arena-rosnav/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/agents/jackal"
+ # burger:
+ # # number of robots of this type
+ # num_robots: 4
+ # # name of hyperparameter file located in the configs/hyperparameters directory
+ # hyperparameter_file: "default.json"
+ # # name of architecture defined in the Policy factory
+ # architecture_name: "AGENT_24"
+ # # path to latest checkpoint; if provided the training will be resumed from that checkpoint
+ # resume: null
+ # add more robots as you wish
+
\ No newline at end of file
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/hyperparameters/default.json b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/hyperparameters/default.json
index aab39666c..c7a84645a 100644
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/hyperparameters/default.json
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/hyperparameters/default.json
@@ -2,7 +2,7 @@
"agent_name": "",
"robot": "myrobot",
"actions_in_observationspace": true,
- "reward_fnc": "rule_05",
+ "reward_fnc": "rule_04",
"discrete_action_space": false,
"normalize": false,
"task_mode": "staged",
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_config.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_config.yaml
new file mode 100644
index 000000000..99ef999c7
--- /dev/null
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_config.yaml
@@ -0,0 +1,68 @@
+# in debug_mode no agent directories will be created and no models will be saved
+debug_mode: true
+
+# Hardware specifc configurations
+# number of parallel environments
+n_envs: 1
+# gpu yes or no
+no_gpu: false
+
+
+### Global configuration that applies to all robots
+# navigation task mode, chose from "random" or "staged"
+task_mode: "staged"
+# number of simulation timesteps
+n_timesteps: 40000000
+max_num_moves_per_eps: 1000
+
+periodic_eval:
+ # max number of steps per episode
+ max_num_moves_per_eps: 1000
+ # number of evaluation episodes
+ n_eval_episodes: 100
+ # evaluation frequency, evaluation after every n_envs * 20000 timesteps
+ eval_freq: 20000
+
+### training_curriculum
+# threshold metric to be considered during evaluation
+# can be either "succ" (success rate) or "rew" (reward)
+training_curriculum:
+ # file for the robot's learning curriculum
+ training_curriculum_file: "training_curriculum.yaml"
+ threshold_type: "succ"
+ upper_threshold: 0.8
+ lower_threshold: 0.6
+
+### stop training on threshold
+# stops training when last stage reached and threshold satisfied
+stop_training:
+ threshold_type: "succ"
+ threshold: 0.9
+
+
+# save evaluation stats during training in log file
+eval_log: false
+# use tensorboard
+tb: false
+
+robots:
+ jackal:
+ # number of robots of this type
+ num_robots: 2
+ # name of hyperparameter file located in the configs/hyperparameters directory
+ hyperparameter_file: "default.json"
+ # name of architecture defined in the Policy factory
+ architecture_name: "AGENT_24"
+ # path to latest checkpoint; if provided the training will be resumed from that checkpoint
+ resume: null
+ # burger:
+ # # number of robots of this type
+ # num_robots: 4
+ # # name of hyperparameter file located in the configs/hyperparameters directory
+ # hyperparameter_file: "default.json"
+ # # name of architecture defined in the Policy factory
+ # architecture_name: "AGENT_24"
+ # # path to latest checkpoint; if provided the training will be resumed from that checkpoint
+ # resume: null
+ # add more robots as you wish
+
\ No newline at end of file
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculum.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculums/training_curriculum.yaml
similarity index 100%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculum.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculums/training_curriculum.yaml
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculum_map1small.yaml b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculums/training_curriculum_map1small.yaml
similarity index 100%
rename from arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculum_map1small.yaml
rename to arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/configs/training_curriculums/training_curriculum_map1small.yaml
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/__pycache__/__init__.cpython-38.pyc b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/__pycache__/__init__.cpython-38.pyc
index cde1f812d..071e0c50a 100644
Binary files a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/__pycache__/__init__.cpython-38.pyc and b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/__pycache__/__init__.cpython-38.pyc differ
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/base_agent_wrapper.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/base_agent_wrapper.py
index ff3e18a6f..f8e3e5f70 100644
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/base_agent_wrapper.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/base_agent_wrapper.py
@@ -16,14 +16,7 @@
from rl_agent.utils.reward import RewardCalculator
robot_model = rospy.get_param("model")
-ROOT_ROBOT_PATH = os.path.join(
- rospkg.RosPack().get_path("simulator_setup"), "robot"
-)
-DEFAULT_ACTION_SPACE = os.path.join(
- rospkg.RosPack().get_path("arena_local_planner_drl"),
- "configs",
- f"default_settings_{robot_model}.yaml",
-)
+ROOT_ROBOT_PATH = os.path.join(rospkg.RosPack().get_path("simulator_setup"), "robot")
DEFAULT_HYPERPARAMETER = os.path.join(
rospkg.RosPack().get_path("arena_local_planner_drl"),
"configs",
@@ -38,39 +31,46 @@ class BaseDRLAgent(ABC):
def __init__(
self,
ns: str = None,
- robot_name: str = None,
+ robot_model: str = "burger",
+ robot_ns: str = None,
hyperparameter_path: str = DEFAULT_HYPERPARAMETER,
- action_space_path: str = DEFAULT_ACTION_SPACE,
- *args,
- **kwargs,
) -> None:
"""[summary]
Args:
ns (str, optional):
Agent name (directory has to be of the same name). Defaults to None.
- robot_name (str, optional):
+ robot_model (str, optional):
+ Robot model name. Defaults to "burger".
+ robot_ns (str, optional):
Robot specific ROS namespace extension. Defaults to None.
hyperparameter_path (str, optional):
Path to json file containing defined hyperparameters.
Defaults to DEFAULT_HYPERPARAMETER.
- action_space_path (str, optional):
- Path to yaml file containing action space settings.
- Defaults to DEFAULT_ACTION_SPACE.
"""
self._is_train_mode = rospy.get_param("/train_mode")
- self._ns = "" if ns is None or ns == "" else ns + "/"
- self._ns_robot = (
- self._ns if robot_name is None else self._ns + robot_name + "/"
- )
- self._robot_sim_ns = robot_name
+ self._ns = "" if ns is None or not ns else f"{ns}/"
+ self._ns_robot = self._ns if robot_ns is None else self._ns + robot_ns
+ self._robot_sim_ns = robot_ns
+
+ self.robot_model = robot_model
- self.load_hyperparameters(path=hyperparameter_path)
robot_setting_path = os.path.join(
- ROOT_ROBOT_PATH, self.robot_config_name + ".model.yaml"
+ ROOT_ROBOT_PATH, f"{self.robot_model}.model.yaml"
)
+
+ action_space_path = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "action_spaces",
+ f"default_settings_{self.robot_model}.yaml",
+ )
+
+ self.load_hyperparameters(path=hyperparameter_path)
self.read_setting_files(robot_setting_path, action_space_path)
+ # self._check_robot_type_from_params()
+
self.setup_action_space()
self.setup_reward_calculator()
@@ -84,13 +84,13 @@ def __init__(
if self._is_train_mode:
# w/o action publisher node
self._action_pub = rospy.Publisher(
- f"{self._ns_robot}cmd_vel", Twist, queue_size=1
+ f"{self._ns_robot}/cmd_vel", Twist, queue_size=1
)
else:
# w/ action publisher node
# (controls action rate being published on '../cmd_vel')
self._action_pub = rospy.Publisher(
- f"{self._ns_robot}cmd_vel_pub", Twist, queue_size=1
+ f"{self._ns_robot}/cmd_vel_pub", Twist, queue_size=1
)
@abstractmethod
@@ -108,15 +108,13 @@ def load_hyperparameters(self, path: str) -> None:
Args:
path (str): Path to the json file.
"""
- assert os.path.isfile(
- path
- ), f"Hyperparameters file cannot be found at {path}!"
+ assert os.path.isfile(path), f"Hyperparameters file cannot be found at {path}!"
with open(path, "r") as file:
hyperparams = json.load(file)
self._agent_params = hyperparams
- self._get_robot_name_from_params()
+ self.robot_config_name = robot_model
rospy.set_param(
"actions_in_obs",
self._agent_params.get("actions_in_observationspace", False),
@@ -139,7 +137,12 @@ def read_setting_files(
"""
self._num_laser_beams = None
self._laser_range = None
- self._robot_radius = rospy.get_param("radius") * 1.05
+
+ with open(action_space_yaml, "r", encoding="utf-8") as target:
+ config = yaml.load(target, Loader=yaml.FullLoader)
+
+ self._robot_radius = config["robot"]["radius"] * 1.05
+
with open(robot_setting_yaml, "r") as fd:
robot_data = yaml.safe_load(fd)
@@ -151,10 +154,8 @@ def read_setting_files(
laser_angle_increment = plugin["angle"]["increment"]
self._num_laser_beams = int(
round(
- (laser_angle_max - laser_angle_min)
- / laser_angle_increment
+ (laser_angle_max - laser_angle_min) / laser_angle_increment
)
- + 1
)
self._laser_range = plugin["range"]
@@ -187,19 +188,21 @@ def read_setting_files(
],
}
- def _get_robot_name_from_params(self):
+ def _check_robot_type_from_params(self):
"""Retrives the agent-specific robot name from the dictionary loaded\
- from respective 'hyperparameter.json'.
+ from respective 'hyperparameter.json' and compares it to the provided
+ robot model from initialization.
"""
assert self._agent_params and self._agent_params["robot"]
- self.robot_config_name = self._agent_params["robot"]
+ assert self.robot_model == self._agent_params["robot"], (
+ "Robot model in hyperparameter.json is not the same as the parsed model!"
+ f"({self.robot_model} != {self._agent_params['robot']})"
+ )
def setup_action_space(self) -> None:
"""Sets up the action space. (spaces.Box)"""
assert self._discrete_actions or self._cont_actions
- assert (
- self._agent_params and "discrete_action_space" in self._agent_params
- )
+ assert self._agent_params and "discrete_action_space" in self._agent_params
if self._agent_params["discrete_action_space"]:
# self._discrete_actions is a list, each element is a dict with the keys ["name", 'linear','angular']
@@ -298,9 +301,7 @@ def normalize_observations(self, merged_obs: np.ndarray) -> np.ndarray:
Returns:
np.ndarray: Normalized observations array.
"""
- assert self._agent_params["normalize"] and hasattr(
- self, "_obs_norm_func"
- )
+ assert self._agent_params["normalize"] and hasattr(self, "_obs_norm_func")
return self._obs_norm_func(merged_obs)
def get_action(self, obs: np.ndarray) -> np.ndarray:
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/envs/__pycache__/__init__.cpython-38.pyc b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/envs/__pycache__/__init__.cpython-38.pyc
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index 17b7b6ca3..a4ba6ebb8 100644
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diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/envs/flatland_gym_env.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/envs/flatland_gym_env.py
index f3fc28340..fb33047ad 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/envs/flatland_gym_env.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/envs/flatland_gym_env.py
@@ -1,10 +1,12 @@
#! /usr/bin/env python3
from operator import is_
+import os
from random import randint
import gym
from gym import spaces
from gym.spaces import space
from typing import Union
+import rospkg
from stable_baselines3.common.env_checker import check_env
import yaml
from rl_agent.utils.observation_collector import ObservationCollector
@@ -68,7 +70,9 @@ def __init__(
ns_int = int(ns.split("_")[1])
time.sleep(ns_int * 2)
except Exception:
- rospy.logwarn(f"Can't not determinate the number of the environment, training script may crash!")
+ rospy.logwarn(
+ f"Can't not determinate the number of the environment, training script may crash!"
+ )
# process specific namespace in ros system
self.ns_prefix = "" if (ns == "" or ns is None) else "/" + ns + "/"
@@ -115,17 +119,25 @@ def __init__(
# action agent publisher
if self._is_train_mode:
- self.agent_action_pub = rospy.Publisher(f"{self.ns_prefix}cmd_vel", Twist, queue_size=1)
+ self.agent_action_pub = rospy.Publisher(
+ f"{self.ns_prefix}cmd_vel", Twist, queue_size=1
+ )
else:
- self.agent_action_pub = rospy.Publisher(f"{self.ns_prefix}cmd_vel_pub", Twist, queue_size=1)
+ self.agent_action_pub = rospy.Publisher(
+ f"{self.ns_prefix}cmd_vel_pub", Twist, queue_size=1
+ )
# service clients
if self._is_train_mode:
self._service_name_step = f"{self.ns_prefix}step_world"
- self._sim_step_client = rospy.ServiceProxy(self._service_name_step, StepWorld)
+ self._sim_step_client = rospy.ServiceProxy(
+ self._service_name_step, StepWorld
+ )
# instantiate task manager
- self.task = get_predefined_task(ns, mode=task_mode, start_stage=kwargs["curr_stage"], PATHS=PATHS)
+ self.task = get_predefined_task(
+ ns, mode=task_mode, start_stage=kwargs["curr_stage"], PATHS=PATHS
+ )
self._steps_curr_episode = 0
self._episode = 0
@@ -153,7 +165,8 @@ def setup_by_configuration(self, robot_yaml_path: str, settings_yaml_path: str):
Args:
robot_yaml_path (str): [description]
"""
- self._robot_radius = rospy.get_param("radius") + 0.25
+ self._robot_radius = yaml.load(settings_yaml_path)["robot"]["radius"] + 0.25
+
with open(robot_yaml_path, "r") as fd:
robot_data = yaml.safe_load(fd)
@@ -163,7 +176,11 @@ def setup_by_configuration(self, robot_yaml_path: str, settings_yaml_path: str):
laser_angle_min = plugin["angle"]["min"]
laser_angle_max = plugin["angle"]["max"]
laser_angle_increment = plugin["angle"]["increment"]
- self._laser_num_beams = int(round((laser_angle_max - laser_angle_min) / laser_angle_increment))
+ self._laser_num_beams = int(
+ round(
+ (laser_angle_max - laser_angle_min) / laser_angle_increment
+ )
+ )
self._laser_max_range = plugin["range"]
with open(settings_yaml_path, "r") as fd:
@@ -173,12 +190,18 @@ def setup_by_configuration(self, robot_yaml_path: str, settings_yaml_path: str):
if self._is_action_space_discrete:
# self._discrete_actions is a list, each element is a dict with the keys ["name", 'linear','angular']
- assert not self._holonomic, "Discrete action space currently not supported for holonomic robots"
+ assert (
+ not self._holonomic
+ ), "Discrete action space currently not supported for holonomic robots"
self._discrete_acitons = setting_data["robot"]["discrete_actions"]
self.action_space = spaces.Discrete(len(self._discrete_acitons))
else:
- linear_range = setting_data["robot"]["continuous_actions"]["linear_range"]
- angular_range = setting_data["robot"]["continuous_actions"]["angular_range"]
+ linear_range = setting_data["robot"]["continuous_actions"][
+ "linear_range"
+ ]
+ angular_range = setting_data["robot"]["continuous_actions"][
+ "angular_range"
+ ]
if not self._holonomic:
self.action_space = spaces.Box(
@@ -218,7 +241,9 @@ def _pub_action(self, action: np.ndarray) -> Twist:
self.agent_action_pub.publish(action_msg)
def _translate_disc_action(self, action):
- assert not self._holonomic, "Discrete action space currently not supported for holonomic robots"
+ assert (
+ not self._holonomic
+ ), "Discrete action space currently not supported for holonomic robots"
new_action = np.array([])
new_action = np.append(new_action, self._discrete_acitons[action]["linear"])
new_action = np.append(new_action, self._discrete_acitons[action]["angular"])
@@ -252,7 +277,9 @@ def step(self, action: np.ndarray):
self._steps_curr_episode += 1
# wait for new observations
- merged_obs, obs_dict = self.observation_collector.get_observations(last_action=self._last_action)
+ merged_obs, obs_dict = self.observation_collector.get_observations(
+ last_action=self._last_action
+ )
self._last_action = action
# calculate reward
@@ -337,7 +364,9 @@ def _update_eval_statistics(self, obs_dict: dict, reward_info: dict):
"""
# distance travelled
if self._last_robot_pose is not None:
- self._distance_travelled += FlatlandEnv.get_distance(self._last_robot_pose, obs_dict["robot_pose"])
+ self._distance_travelled += FlatlandEnv.get_distance(
+ self._last_robot_pose, obs_dict["robot_pose"]
+ )
# collision detector
if "crash" in reward_info:
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diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/model/__pycache__/feature_extractors.cpython-38.pyc b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/model/__pycache__/feature_extractors.cpython-38.pyc
index 3e0d55ca5..f4226141a 100644
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diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/model/agent_factory.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/model/agent_factory.py
index 945f9627d..cd381e86d 100644
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/model/agent_factory.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/model/agent_factory.py
@@ -1,3 +1,4 @@
+from warnings import warn
from typing import Callable, Type, Union
from stable_baselines3.common.policies import BasePolicy
@@ -24,7 +25,11 @@ def register(cls, name: str) -> Callable:
"""
def inner_wrapper(wrapped_class) -> Callable:
- assert name not in cls.registry, f"Agent '{name}' already exists!"
+ if name in cls.registry:
+ warn(
+ f"Try to register agent: '{name}' although an entry already exists. "
+ "Register entry will be overwritten if there exists another architecture with the same id."
+ )
assert issubclass(wrapped_class, BaseAgent) or issubclass(
wrapped_class, BasePolicy
), f"Wrapped class {wrapped_class.__name__} is neither of type 'BaseAgent' nor 'BasePolicy!'"
@@ -54,7 +59,7 @@ def instantiate(cls, name: str, **kwargs) -> Union[Type[BaseAgent], Type[BasePol
"""
assert name in cls.registry, f"Agent '{name}' is not registered!"
agent_class = cls.registry[name]
-
+
if issubclass(agent_class, BaseAgent):
return agent_class(**kwargs)
else:
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/training_agent_wrapper.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/training_agent_wrapper.py
index cbb49bea1..edbf5746d 100644
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/training_agent_wrapper.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/training_agent_wrapper.py
@@ -8,23 +8,16 @@
from geometry_msgs.msg import Twist
-robot_model = rospy.get_param("model")
-DEFAULT_ACTION_SPACE = os.path.join(
- rospkg.RosPack().get_path("arena_local_planner_drl"),
- "configs",
- f"default_settings_{robot_model}.yaml",
-)
-
class TrainingDRLAgent(BaseDRLAgent):
def __init__(
self,
ns: str,
- robot_name: str,
+ robot_model: str,
+ robot_ns: str,
hyperparameter_path: str,
- action_space_path: str = DEFAULT_ACTION_SPACE,
*args,
- **kwargs
+ **kwargs,
) -> None:
"""DRL Agent Wrapper Class for training.
@@ -51,7 +44,7 @@ def __init__(
Args:
ns (str, optional):
Agent name (directory has to be of the same name). Defaults to None.
- robot_name (str, optional):
+ robot_ns (str, optional):
Robot specific ROS namespace extension. Defaults to None.
hyperparameter_path (str, optional):
Path to json file containing defined hyperparameters.
@@ -62,11 +55,11 @@ def __init__(
"""
super().__init__(
ns=ns,
- robot_name=robot_name,
+ robot_model=robot_model,
+ robot_ns=robot_ns,
hyperparameter_path=hyperparameter_path,
- action_space_path=action_space_path,
*args,
- **kwargs
+ **kwargs,
)
def setup_agent(self) -> None:
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/observation_collector.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/observation_collector.py
index 089b0d908..9a48a07f2 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/observation_collector.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/observation_collector.py
@@ -69,9 +69,7 @@ def __init__(
dtype=np.float32,
),
spaces.Box(low=0, high=15, shape=(1,), dtype=np.float32),
- spaces.Box(
- low=-np.pi, high=np.pi, shape=(1,), dtype=np.float32
- ),
+ spaces.Box(low=-np.pi, high=np.pi, shape=(1,), dtype=np.float32),
)
)
else:
@@ -83,9 +81,7 @@ def __init__(
shape=(num_lidar_beams,),
dtype=np.float32,
),
- spaces.Box(
- low=0, high=15, shape=(1,), dtype=np.float32
- ), # rho
+ spaces.Box(low=0, high=15, shape=(1,), dtype=np.float32), # rho
spaces.Box(
low=-np.pi,
high=np.pi,
@@ -123,9 +119,7 @@ def __init__(
# synchronization parameters
self._ext_time_sync = external_time_sync
- self._first_sync_obs = (
- True # whether to return first sync'd obs or most recent
- )
+ self._first_sync_obs = True # whether to return first sync'd obs or most recent
self.max_deque_size = 10
self._sync_slop = 0.05
@@ -167,18 +161,22 @@ def __init__(
# self._clock_sub = rospy.Subscriber(
# f'{self.ns_prefix}clock', Clock, self.callback_clock, tcp_nodelay=True)
-
+ goal_topic = (
+ f"{self.ns_prefix}subgoal"
+ if rospy.get_param("num_robots", default=1) == 1
+ else f"{self.ns_prefix}goal"
+ )
self._subgoal_sub = rospy.Subscriber(
- f"{self.ns_prefix}subgoal", PoseStamped, self.callback_subgoal
+ goal_topic, PoseStamped, self.callback_subgoal
)
-
self._globalplan_sub = rospy.Subscriber(
f"{self.ns_prefix}globalPlan", Path, self.callback_global_plan
)
# service clients
if self._is_train_mode:
- self._service_name_step = f"{self.ns_prefix}step_world"
+ _sim_namespace = self.ns_prefix.split("/")[1]
+ self._service_name_step = f"/{_sim_namespace}/step_world"
self._sim_step_client = rospy.ServiceProxy(
self._service_name_step, StepWorld
)
@@ -244,9 +242,9 @@ def _get_goal_pose_in_robot_frame(goal_pos: Pose2D, robot_pos: Pose2D):
y_relative = goal_pos.y - robot_pos.y
x_relative = goal_pos.x - robot_pos.x
rho = (x_relative ** 2 + y_relative ** 2) ** 0.5
- theta = (
- np.arctan2(y_relative, x_relative) - robot_pos.theta + 4 * np.pi
- ) % (2 * np.pi) - np.pi
+ theta = (np.arctan2(y_relative, x_relative) - robot_pos.theta + 4 * np.pi) % (
+ 2 * np.pi
+ ) - np.pi
return rho, theta
def get_sync_obs(self):
@@ -315,9 +313,7 @@ def callback_subgoal(self, msg_Subgoal):
return
def callback_global_plan(self, msg_global_plan):
- self._globalplan = ObservationCollector.process_global_plan_msg(
- msg_global_plan
- )
+ self._globalplan = ObservationCollector.process_global_plan_msg(msg_global_plan)
return
def callback_scan(self, msg_laserscan):
@@ -330,9 +326,7 @@ def callback_robot_state(self, msg_robotstate):
self._rs_deque.popleft()
self._rs_deque.append(msg_robotstate)
- def callback_observation_received(
- self, msg_LaserScan, msg_RobotStateStamped
- ):
+ def callback_observation_received(self, msg_LaserScan, msg_RobotStateStamped):
# process sensor msg
self._scan = self.process_scan_msg(msg_LaserScan)
self._robot_pose, self._robot_vel = self.process_robot_state_msg(
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/reward.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/reward.py
index f3c212f9c..89e667d0f 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/reward.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/utils/reward.py
@@ -9,7 +9,7 @@
class RewardCalculator:
def __init__(
self,
- holonomic: float,
+ holonomic: bool,
robot_radius: float,
safe_dist: float,
goal_radius: float,
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/scripts/training/train_agent.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/scripts/training/train_agent.py
index af281c3f3..2a8702901 100755
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/scripts/training/train_agent.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/scripts/training/train_agent.py
@@ -20,6 +20,7 @@
def main():
+ # parse name of training config file
args, _ = parse_training_args()
# in debug mode, we emulate multiprocessing on only one process
@@ -51,11 +52,19 @@ def main():
# when debug run on one process only
if not args.debug and ns_for_nodes:
env = SubprocVecEnv(
- [make_envs(args, ns_for_nodes, i, params=params, PATHS=PATHS) for i in range(args.n_envs)],
+ [
+ make_envs(args, ns_for_nodes, i, params=params, PATHS=PATHS)
+ for i in range(args.n_envs)
+ ],
start_method="fork",
)
else:
- env = DummyVecEnv([make_envs(args, ns_for_nodes, i, params=params, PATHS=PATHS) for i in range(args.n_envs)])
+ env = DummyVecEnv(
+ [
+ make_envs(args, ns_for_nodes, i, params=params, PATHS=PATHS)
+ for i in range(args.n_envs)
+ ]
+ )
# threshold settings for training curriculum
# type can be either 'succ' or 'rew'
@@ -69,7 +78,9 @@ def main():
)
# stop training on reward threshold callback
- stoptraining_cb = StopTrainingOnRewardThreshold(treshhold_type="succ", threshold=0.95, verbose=1)
+ stoptraining_cb = StopTrainingOnRewardThreshold(
+ treshhold_type="succ", threshold=0.95, verbose=1
+ )
# instantiate eval environment
# take task_manager from first sim (currently evaluation only provided for single process)
@@ -113,7 +124,9 @@ def main():
model = PPO(
"MlpPolicy",
env,
- policy_kwargs=dict(net_arch=args.net_arch, activation_fn=get_act_fn(args.act_fn)),
+ policy_kwargs=dict(
+ net_arch=args.net_arch, activation_fn=get_act_fn(args.act_fn)
+ ),
gamma=params["gamma"],
n_steps=params["n_steps"],
ent_coef=params["ent_coef"],
@@ -128,7 +141,9 @@ def main():
verbose=1,
)
elif args.agent is not None:
- agent: Union[Type[BaseAgent], Type[ActorCriticPolicy]] = AgentFactory.instantiate(args.agent)
+ agent: Union[
+ Type[BaseAgent], Type[ActorCriticPolicy]
+ ] = AgentFactory.instantiate(args.agent)
if isinstance(agent, BaseAgent):
model = PPO(
agent.type.value,
@@ -166,7 +181,8 @@ def main():
)
else:
raise TypeError(
- f"Registered agent class {args.agent} is neither of type" "'BaseAgent' or 'ActorCriticPolicy'!"
+ f"Registered agent class {args.agent} is neither of type"
+ "'BaseAgent' or 'ActorCriticPolicy'!"
)
else:
# load flag
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/__init__.cpython-38.pyc b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/__init__.cpython-38.pyc
index 3da04eff7..56b64218c 100644
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diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/argsparser.cpython-38.pyc b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/argsparser.cpython-38.pyc
index 047799d7d..fcd858fe1 100644
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diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/staged_train_callback.cpython-38.pyc b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/staged_train_callback.cpython-38.pyc
index 3151cf5ed..69e95e7db 100644
Binary files a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/staged_train_callback.cpython-38.pyc and b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/__pycache__/staged_train_callback.cpython-38.pyc differ
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/argsparser.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/argsparser.py
index 2d1860112..1abe17a76 100644
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/argsparser.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/argsparser.py
@@ -7,59 +7,79 @@
def training_args(parser):
"""program arguments training script"""
- parser.add_argument(
- "--n_envs", type=int, default=1, help="number of parallel environments"
- )
- parser.add_argument(
- "--no-gpu", action="store_true", help="disables gpu for training"
- )
- parser.add_argument(
- "--debug",
- action="store_true",
- help="disables multiprocessing in order to debug",
- )
- group = parser.add_mutually_exclusive_group(required=True)
-
- import rl_agent.model.custom_policy
- import rl_agent.model.custom_sb3_policy
- from rl_agent.model.agent_factory import AgentFactory
-
- group.add_argument(
- "--agent",
- type=str,
- choices=AgentFactory.registry.keys(),
- help="predefined agent to train",
- )
- group.add_argument(
- "--custom-mlp",
- action="store_true",
- help="enables training with custom multilayer perceptron",
- )
- group.add_argument(
- "--load",
- type=str,
- metavar="[agent name]",
- help="agent to be loaded for training",
- )
parser.add_argument(
"--config",
type=str,
metavar="[config name]",
- default="default",
- help="name of the json file containing" "the hyperparameters",
- )
- parser.add_argument(
- "--n", type=int, help="timesteps in total to be generated for training"
- )
- parser.add_argument(
- "-log",
- "--eval_log",
- action="store_true",
- help="enables storage of evaluation data",
- )
- parser.add_argument(
- "--tb", action="store_true", help="enables tensorboard logging"
- )
+ default="training_config.yaml",
+ help="name of the config file",
+ )
+
+ ###########################################################################
+
+ # parser.add_argument(
+ # "--n_envs", type=int, default=1, help="number of parallel environments"
+ # )
+ # parser.add_argument(
+ # "--no-gpu", action="store_true", help="disables gpu for training"
+ # )
+ # parser.add_argument(
+ # "--debug",
+ # action="store_true",
+ # help="disables multiprocessing in order to debug",
+ # )
+ # group = parser.add_mutually_exclusive_group(required=True)
+
+ # import rl_agent.model.custom_policy
+ # import rl_agent.model.custom_sb3_policy
+ # from rl_agent.model.agent_factory import AgentFactory
+
+ # group.add_argument(
+ # "--agent",
+ # type=str,
+ # choices=AgentFactory.registry.keys(),
+ # help="predefined agent to train",
+ # )
+ # group.add_argument(
+ # "--custom-mlp",
+ # action="store_true",
+ # help="enables training with custom multilayer perceptron",
+ # )
+ # group.add_argument(
+ # "--load",
+ # type=str,
+ # metavar="[agent name]",
+ # help="agent to be loaded for training",
+ # )
+ # parser.add_argument(
+ # "--config",
+ # type=str,
+ # metavar="[config name]",
+ # default="default",
+ # help="name of the json file containing" "the hyperparameters",
+ # )
+ # parser.add_argument(
+ # "--n", type=int, help="timesteps in total to be generated for training"
+ # )
+ # parser.add_argument(
+ # "-log",
+ # "--eval_log",
+ # action="store_true",
+ # help="enables storage of evaluation data",
+ # )
+ # parser.add_argument("--tb", action="store_true", help="enables tensorboard logging")
+
+
+# def marl_training_args(parser):
+# parser.add_argument("--robots", type=int, default=1, help="number of robots")
+
+
+# def parse_marl_training_args(args=None, ignore_unknown=False):
+# """parser for training script"""
+# arg_populate_funcs = [training_args, custom_mlp_args, marl_training_args]
+# arg_check_funcs = [process_training_args]
+
+# return parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown)
def run_agent_args(parser):
@@ -143,21 +163,17 @@ def custom_mlp_args(parser):
def process_training_args(parsed_args):
"""argument check function"""
- if parsed_args.no_gpu:
- os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
- if parsed_args.custom_mlp:
- setattr(parsed_args, "net_arch", get_net_arch(parsed_args))
- else:
- if (
- parsed_args.body != ""
- or parsed_args.pi != ""
- or parsed_args.vf != ""
- ):
- print("[custom mlp] arguments will be ignored..")
- delattr(parsed_args, "body")
- delattr(parsed_args, "pi")
- delattr(parsed_args, "vf")
- delattr(parsed_args, "act_fn")
+ # if parsed_args.no_gpu:
+ # os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+ # if parsed_args.custom_mlp:
+ # setattr(parsed_args, "net_arch", get_net_arch(parsed_args))
+ # else:
+ # if parsed_args.body != "" or parsed_args.pi != "" or parsed_args.vf != "":
+ # print("[custom mlp] arguments will be ignored..")
+ # delattr(parsed_args, "body")
+ # delattr(parsed_args, "pi")
+ # delattr(parsed_args, "vf")
+ # delattr(parsed_args, "act_fn")
def process_run_agent_args(parsed_args):
@@ -170,9 +186,7 @@ def parse_training_args(args=None, ignore_unknown=False):
arg_populate_funcs = [training_args, custom_mlp_args]
arg_check_funcs = [process_training_args]
- return parse_various_args(
- args, arg_populate_funcs, arg_check_funcs, ignore_unknown
- )
+ return parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown)
def parse_run_agent_args(args=None, ignore_unknown=False):
@@ -180,14 +194,10 @@ def parse_run_agent_args(args=None, ignore_unknown=False):
arg_populate_funcs = [run_agent_args]
arg_check_funcs = [process_run_agent_args]
- return parse_various_args(
- args, arg_populate_funcs, arg_check_funcs, ignore_unknown
- )
+ return parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown)
-def parse_various_args(
- args, arg_populate_funcs, arg_check_funcs, ignore_unknown
-):
+def parse_various_args(args, arg_populate_funcs, arg_check_funcs, ignore_unknown):
"""generic arg parsing function"""
parser = argparse.ArgumentParser()
diff --git a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/train_agent_utils.py b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/train_agent_utils.py
index 74c2af519..c7ae5a533 100644
--- a/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/train_agent_utils.py
+++ b/arena_navigation/arena_local_planner/learning_based/arena_local_planner_drl/tools/train_agent_utils.py
@@ -5,6 +5,8 @@
import gym
import json
import os, rospy
+
+import yaml
import rosnode
import rospkg
import time
@@ -83,6 +85,21 @@
}
+def load_config(config_name: str) -> dict:
+ """
+ Load config parameters from config file
+ """
+ config_location = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ config_name + ".yaml",
+ )
+ with open(config_location, "r", encoding="utf-8") as target:
+ config = yaml.load(target, Loader=yaml.FullLoader)
+
+ return config
+
+
def initialize_hyperparameters(
PATHS: dict, load_target: str, config_name: str = "default", n_envs: int = 1
) -> dict:
@@ -117,9 +134,7 @@ def initialize_hyperparameters(
# dynamically adapt n_steps according to batch size and n envs
# then update .json
- check_batch_size(
- n_envs, hyperparams["batch_size"], hyperparams["m_batch_size"]
- )
+ check_batch_size(n_envs, hyperparams["batch_size"], hyperparams["m_batch_size"])
hyperparams["n_steps"] = int(hyperparams["batch_size"] / n_envs)
write_hyperparameters_json(hyperparams, PATHS)
print_hyperparameters(hyperparams)
@@ -151,9 +166,7 @@ def load_hyperparameters_json(
:param config_name: file name of json file when training from scratch
"""
if from_scratch:
- doc_location = os.path.join(
- PATHS.get("hyperparams"), config_name + ".json"
- )
+ doc_location = os.path.join(PATHS.get("hyperparams"), config_name + ".json")
else:
doc_location = os.path.join(PATHS.get("model"), "hyperparameters.json")
@@ -165,8 +178,7 @@ def load_hyperparameters_json(
else:
if from_scratch:
raise FileNotFoundError(
- "Found no '%s.json' in %s"
- % (config_name, PATHS.get("hyperparams"))
+ "Found no '%s.json' in %s" % (config_name, PATHS.get("hyperparams"))
)
else:
raise FileNotFoundError(
@@ -230,9 +242,7 @@ def check_hyperparam_format(loaded_hyperparams: dict, PATHS: dict) -> None:
"actions_in_observationspace" in loaded_hyperparams
and type(loaded_hyperparams["actions_in_observationspace"]) is not bool
):
- raise TypeError(
- "Parameter 'actions_in_observationspace' has to be a boolean!"
- )
+ raise TypeError("Parameter 'actions_in_observationspace' has to be a boolean!")
def update_hyperparam_model(
@@ -336,9 +346,7 @@ def get_paths(agent_name: str, args: argparse.Namespace) -> dict:
PATHS = {
"model": os.path.join(dir, "agents", agent_name),
"tb": os.path.join(dir, "training_logs", "tensorboard", agent_name),
- "eval": os.path.join(
- dir, "training_logs", "train_eval_log", agent_name
- ),
+ "eval": os.path.join(dir, "training_logs", "train_eval_log", agent_name),
"robot_setting": os.path.join(
rospkg.RosPack().get_path("simulator_setup"),
"robot",
@@ -346,10 +354,10 @@ def get_paths(agent_name: str, args: argparse.Namespace) -> dict:
),
"hyperparams": os.path.join(dir, "configs", "hyperparameters"),
"robot_as": os.path.join(
- dir, "configs", f"default_settings_{robot_model}.yaml"
+ dir, "configs", "action_spaces", f"default_settings_{robot_model}.yaml"
),
"curriculum": os.path.join(
- dir, "configs", "training_curriculum_map1small.yaml"
+ dir, "configs", "training_curriculums", "training_curriculum_map1small.yaml"
),
}
# check for mode
@@ -485,9 +493,7 @@ def wait_for_nodes(
from stable_baselines3.common.vec_env.base_vec_env import VecEnv
-def load_vec_normalize(
- params: dict, PATHS: dict, env: VecEnv, eval_env: VecEnv
-):
+def load_vec_normalize(params: dict, PATHS: dict, env: VecEnv, eval_env: VecEnv):
if params["normalize"]:
load_path = os.path.join(PATHS["model"], "vec_normalize.pkl")
if os.path.isfile(load_path):
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 000000000..3ebb20867
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3024 @@
+[[package]]
+name = "absl-py"
+version = "1.0.0"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "actionlib"
+version = "1.12.0"
+description = "The actionlib stack provides a standardized interface for interfacing with preemptable tasks. Examples of this include moving the base to a target location, performing a laser scan and retu..."
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.source]
+type = "legacy"
+url = "https://rospypi.github.io/simple"
+reference = "ros"
+
+[[package]]
+name = "actionlib-msgs"
+version = "1.13.0.post3"
+description = ""
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+genpy = ">=0.6.14,<2000"
+
+[package.source]
+type = "legacy"
+url = "https://rospypi.github.io/simple"
+reference = "ros"
+
+[[package]]
+name = "appnope"
+version = "0.1.2"
+description = "Disable App Nap on macOS >= 10.9"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "argon2-cffi"
+version = "21.1.0"
+description = "The secure Argon2 password hashing algorithm."
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+cffi = ">=1.0.0"
+
+[package.extras]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "furo", "wheel", "pre-commit"]
+docs = ["sphinx", "furo"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
+
+[[package]]
+name = "attrs"
+version = "21.2.0"
+description = "Classes Without Boilerplate"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[package.extras]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit"]
+docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
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+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins"]
+
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+
+[[package]]
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+
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+
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+
+[package.dependencies]
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+
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+
+[package.source]
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+
+[package.dependencies]
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+
+[[package]]
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+version = "2021.10.8"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
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+description = "Foreign Function Interface for Python calling C code."
+category = "main"
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+
+[package.dependencies]
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+
+[[package]]
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+category = "main"
+optional = false
+python-versions = ">=3.5.0"
+
+[package.extras]
+unicode_backport = ["unicodedata2"]
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+[package.dependencies]
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+category = "main"
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+
+[[package]]
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+
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+category = "main"
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+
+[[package]]
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+category = "main"
+optional = false
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+
+[[package]]
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+optional = false
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+
+[[package]]
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+[[package]]
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+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
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+category = "main"
+optional = false
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+
+[[package]]
+name = "entrypoints"
+version = "0.3"
+description = "Discover and load entry points from installed packages."
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+optional = false
+python-versions = ">=2.7"
+
+[[package]]
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+version = "3.3.2"
+description = "A platform independent file lock."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+docs = ["furo (>=2021.8.17b43)", "sphinx (>=4.1)", "sphinx-autodoc-typehints (>=1.12)"]
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+
+[[package]]
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+category = "main"
+optional = false
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+
+[package.source]
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+url = "https://rospypi.github.io/simple"
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+[package.dependencies]
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+[package.source]
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+[package.dependencies]
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+
+[package.source]
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+url = "https://rospypi.github.io/simple"
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+[package.dependencies]
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+
+[package.extras]
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+
+[[package]]
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+
+[package.dependencies]
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+[package.extras]
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+
+[package.dependencies]
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+
+[package.extras]
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+
+[package.dependencies]
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+[package.extras]
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+[package.dependencies]
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+[package.dependencies]
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+
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+category = "main"
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+
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..17871ff86
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,40 @@
+[tool.poetry]
+name = "arena-rosnav"
+version = "0.1.0"
+description = ""
+authors = ["Your Name "]
+
+[[tool.poetry.source]]
+name = "ros"
+url = "https://rospypi.github.io/simple/"
+secondary = true
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.10"
+rospy = "^1.15.7"
+rosbag = "^1.15.7"
+tf = "^1.12.1"
+tf2-ros = "^0.6.5"
+PyYAML = "^5.4.1"
+catkin-pkg = "^0.4.23"
+netifaces = "^0.10.9"
+pathlib = "^1.0.1"
+empy = "^3.3.4"
+setuptools = "^56.2.0"
+filelock = "^3.0.12"
+PyQt5 = "^5.15.4"
+scipy = "^1.7.1"
+PettingZoo = "1.13.1"
+SuperSuit = "^3.3.0"
+stable-baselines3 = {git = "https://github.com/ignc-research/stable-baselines3", rev = "marl"}
+tensorboard = "^2.7.0"
+notebook = "^6.4.5"
+jupyter = "^1.0.0"
+
+[tool.poetry.dev-dependencies]
+pdoc3 = "^0.10.0"
+black = {version = "^22.3.0", allow-prereleases = true}
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
diff --git a/task_generator/scripts/__pycache__/clear_costmap.cpython-38.pyc b/task_generator/scripts/__pycache__/clear_costmap.cpython-38.pyc
index 7746ab08d..83286bbc7 100644
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diff --git a/task_generator/task_generator/__pycache__/__init__.cpython-38.pyc b/task_generator/task_generator/__pycache__/__init__.cpython-38.pyc
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diff --git a/task_generator/task_generator/__pycache__/obstacles_manager.cpython-38.pyc b/task_generator/task_generator/__pycache__/obstacles_manager.cpython-38.pyc
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diff --git a/task_generator/task_generator/__pycache__/robot_manager.cpython-38.pyc b/task_generator/task_generator/__pycache__/robot_manager.cpython-38.pyc
index 8149f67f5..f8ac9e363 100644
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diff --git a/task_generator/task_generator/__pycache__/tasks.cpython-38.pyc b/task_generator/task_generator/__pycache__/tasks.cpython-38.pyc
index c1b60355d..812775137 100644
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diff --git a/task_generator/task_generator/__pycache__/utils.cpython-38.pyc b/task_generator/task_generator/__pycache__/utils.cpython-38.pyc
index d032aeb82..53fc29e7f 100644
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diff --git a/task_generator/task_generator/marl_robot_manager.py b/task_generator/task_generator/marl_robot_manager.py
new file mode 100755
index 000000000..414eec98e
--- /dev/null
+++ b/task_generator/task_generator/marl_robot_manager.py
@@ -0,0 +1,296 @@
+# from math import ceil, sqrt
+from typing import Union
+
+import math
+import os
+
+from torch.nn.modules.module import T
+import rospy
+import rospkg
+import tf
+import yaml
+
+from flatland_msgs.srv import (
+ MoveModel,
+ MoveModelRequest,
+ SpawnModelRequest,
+ SpawnModel,
+ StepWorld,
+)
+from geometry_msgs.msg import Pose2D, PoseStamped
+from nav_msgs.msg import OccupancyGrid
+
+from .utils import generate_freespace_indices, get_random_pos_on_map
+
+
+class RobotManager:
+ """
+ A manager class using flatland provided services to spawn, move and delete Robot. Currently only one robot
+ is managed
+ """
+
+ def __init__(
+ self,
+ ns: str,
+ map_: OccupancyGrid,
+ robot_yaml_path: str,
+ robot_type: str,
+ robot_id: str = "myrobot",
+ timeout=20,
+ ) -> None:
+ """[summary]
+
+ Args:
+ ns(namespace): if ns == '', we will use global namespace
+ map_ (OccupancyGrid): the map info
+ robot_yaml_path (str): the file name of the base robot yaml file.
+
+ """
+ """SET A METHOD TO EXTRACT IF MARL IS BEIN REQUESTED"""
+ MARL = rospy.get_param("num_robots") > 1
+
+ self.ns = ns
+ self.ns_prefix = "" if ns == "" else "/" + ns + "/"
+
+ self.robot_id = robot_id
+ self.robot_type = robot_type
+
+ self.is_training_mode = rospy.get_param("/train_mode")
+ self.step_size = rospy.get_param("step_size")
+
+ self._get_robot_config(robot_yaml_path)
+ robot_yaml_path = (
+ self._generate_robot_config_with_adjusted_topics()
+ if MARL
+ else robot_yaml_path
+ )
+
+ # setup proxy to handle services provided by flatland
+ rospy.wait_for_service(f"{self.ns_prefix}move_model", timeout=timeout)
+ rospy.wait_for_service(f"{self.ns_prefix}spawn_model", timeout=timeout)
+
+ self._srv_move_model = rospy.ServiceProxy(
+ f"{self.ns_prefix}move_model", MoveModel
+ )
+ self._srv_spawn_model = rospy.ServiceProxy(
+ f"{self.ns_prefix}spawn_model", SpawnModel
+ )
+ # it's only needed in training mode to send the clock signal.
+ self._step_world = rospy.ServiceProxy(f"{self.ns_prefix}step_world", StepWorld)
+
+ # publisher
+ goal_topic = (
+ f"{self.ns_prefix}{self.robot_id}/goal" if MARL else f"{self.ns_prefix}goal"
+ )
+ self._goal_pub = rospy.Publisher(
+ f"{goal_topic}", PoseStamped, queue_size=1, latch=True
+ )
+
+ self.update_map(map_)
+ self._spawn_robot(robot_yaml_path)
+
+ # remove temporary config file
+ if MARL:
+ os.remove(robot_yaml_path)
+
+ def _spawn_robot(self, robot_yaml_path: str) -> None:
+ request = SpawnModelRequest()
+ request.yaml_path = robot_yaml_path
+ request.name = self.robot_id
+ request.ns = self.ns
+ self._srv_spawn_model(request)
+
+ def _get_robot_config(self, robot_yaml_path: str) -> None:
+ """Get robot info e.g robot name, radius, Laser related infomation
+
+ Args:
+ robot_yaml_path ([type]): [description]
+ """
+ robot_config = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "action_spaces",
+ "default_settings_" + self.robot_type + ".yaml",
+ )
+ with open(robot_config, "r", encoding="utf-8") as target:
+ config = yaml.load(target, Loader=yaml.FullLoader)
+ self.ROBOT_RADIUS = config["robot"]["radius"]
+
+ with open(robot_yaml_path, "r") as f:
+ self._robot_data = yaml.safe_load(f)
+
+ # get laser_update_rate
+ for plugin in self._robot_data["plugins"]:
+ if plugin["type"] == "Laser":
+ self.LASER_UPDATE_RATE = plugin.setdefault("update_rate", 1)
+
+ def _generate_robot_config_with_adjusted_topics(self) -> str:
+ """Generates a robot-specific config file (yaml) where the publication \
+ and subscription topics are adjusted with the robot's namespace.
+
+ Returns:
+ str: Path of the robot-specific adjusted config file.
+
+ Note:
+ - The namespaces consist of: [simulation ns] / [robot name] / *topic*\
+ e.g.: sim_1/myrobot/scan
+ - The yaml files are temporarily dumped into *../simulator_setup/tmp_robot_configs*
+ """
+ self._robot_data["bodies"][0]["name"] = (
+ self.robot_id + "_" + self._robot_data["bodies"][0]["name"]
+ )
+
+ for plugin in self._robot_data["plugins"]:
+ if plugin["type"] == "DiffDrive":
+ plugin["body"] = self._robot_data["bodies"][0]["name"]
+ plugin["odom_frame_id"] = self.robot_id + "_" + plugin["odom_frame_id"]
+ plugin["odom_pub"] = self.robot_id + "/" + plugin["odom_pub"]
+ plugin["twist_sub"] = (
+ self.robot_id + "/" + plugin.get("twist_sub", "cmd_vel")
+ )
+
+ elif plugin["type"] == "Laser":
+ plugin["topic"] = self.robot_id + "/" + plugin["topic"]
+ plugin["body"] = self._robot_data["bodies"][0]["name"]
+ plugin["frame"] = self.robot_id + "_" + plugin["frame"]
+
+ tmp_folder_path = os.path.join(
+ rospkg.RosPack().get_path("simulator_setup"), "tmp_robot_configs"
+ )
+ os.makedirs(tmp_folder_path, exist_ok=True)
+ tmp_config_name = self.ns + self.robot_id + ".robot_config.yaml"
+ tmp_config_path = os.path.join(tmp_folder_path, tmp_config_name)
+
+ with open(tmp_config_path, "w") as fd:
+ yaml.dump(self._robot_data, fd)
+
+ return tmp_config_path
+
+ def update_map(self, new_map: OccupancyGrid):
+ self.map = new_map
+ # a tuple stores the indices of the non-occupied spaces. format ((y,....),(x,...)
+ self._free_space_indices = generate_freespace_indices(self.map)
+
+ def move_robot(self, pose: Pose2D):
+ """Move the robot to a given position.
+
+ Args:
+ pose (Pose2D): Target postion.
+ """
+ # call service move_model
+
+ srv_request = MoveModelRequest()
+ srv_request.name = self.robot_id
+ srv_request.pose = pose
+
+ # call service
+ self._srv_move_model(srv_request)
+ if self.is_training_mode:
+ # a necessaray procedure to let the flatland publish the
+ # laser,odom's Transformation, which are needed for creating
+ # global path
+ # assert self.step_size * \
+ # self.LASER_UPDATE_RATE == 1, f"TO run the traning successfully, make sure the laser_update_rate*step_size == 1 \
+ # \n\tcurrent step_size:\t {self.step_size}\n\tcurrent laser's update rate:\t {self.LASER_UPDATE_RATE} "
+ for _ in range(math.ceil(1 / (self.step_size * self.LASER_UPDATE_RATE))):
+ self._step_world()
+
+ def set_start_pos_random(self):
+ start_pos = Pose2D()
+ start_pos.x, start_pos, start_pos.theta = get_random_pos_on_map(
+ self._free_space_indices, self.map, self.ROBOT_RADIUS * 2
+ )
+ self.move_robot(start_pos)
+
+ def set_start_pos_goal_pos(
+ self,
+ start_pos: Union[Pose2D, None] = None,
+ goal_pos: Union[Pose2D, None] = None,
+ min_dist=1,
+ forbidden_zones: Union[list, None] = None,
+ ):
+ """Set up start position and the goal postion. Path validation checking will be conducted. If it failed, an
+ exception will be raised.
+
+ Args:
+ start_pos (Union[Pose2D,None], optional): start position. if None, it will be set randomly. Defaults to None.
+ goal_pos (Union[Pose2D,None], optional): [description]. if None, it will be set randomly .Defaults to None.
+ min_dist (float): minimum distance between start_pos and goal_pos
+ forbidden_zones (list): a list of tuples with the format (x,y,r), where the the robot should not be reset.
+ Exception:
+ Exception("can not generate a path with the given start position and the goal position of the robot")
+ """
+
+ def dist(x1, y1, x2, y2):
+ return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
+
+ if start_pos is None or goal_pos is None:
+ # if any of them need to be random generated, we set a higher threshold,otherwise only try once
+ max_try_times = 20
+ else:
+ max_try_times = 1
+
+ i_try = 0
+ start_pos_ = None
+ goal_pos_ = None
+ while i_try < max_try_times:
+
+ if start_pos is None:
+ start_pos_ = Pose2D()
+ (start_pos_.x, start_pos_.y, start_pos_.theta,) = get_random_pos_on_map(
+ self._free_space_indices,
+ self.map,
+ self.ROBOT_RADIUS * 2,
+ forbidden_zones=forbidden_zones,
+ )
+ else:
+ start_pos_ = start_pos
+
+ if goal_pos is None:
+ goal_pos_ = Pose2D()
+ (goal_pos_.x, goal_pos_.y, goal_pos_.theta,) = get_random_pos_on_map(
+ self._free_space_indices, self.map, self.ROBOT_RADIUS * 2
+ )
+ else:
+ goal_pos_ = goal_pos
+
+ if dist(start_pos_.x, start_pos_.y, goal_pos_.x, goal_pos_.y) < min_dist:
+ i_try += 1
+ continue
+ # move the robot to the start pos
+ self.move_robot(start_pos_)
+ try:
+ # publish the goal, if the gobal plath planner can't generate a path, a, exception will be raised.
+ self.publish_goal(goal_pos_.x, goal_pos_.y, goal_pos_.theta)
+ break
+ except rospy.ServiceException:
+ i_try += 1
+ if i_try == max_try_times:
+ # TODO Define specific type of Exception
+ raise rospy.ServiceException(
+ "can not generate a path with the given start position and the goal position of the robot"
+ )
+ else:
+ return start_pos_, goal_pos_
+
+ def publish_goal(self, x, y, theta):
+ """
+ Publishing goal (x, y, theta)
+ :param x x-position of the goal
+ :param y y-position of the goal
+ :param theta theta-position of the goal
+ """
+ goal = PoseStamped()
+ goal.header.stamp = rospy.get_rostime()
+ goal.header.frame_id = "map"
+ goal.pose.position.x = x
+ goal.pose.position.y = y
+ quaternion = tf.transformations.quaternion_from_euler(0, 0, 0)
+ goal.pose.orientation.w = quaternion[0]
+ goal.pose.orientation.x = quaternion[1]
+ goal.pose.orientation.y = quaternion[2]
+ goal.pose.orientation.z = quaternion[3]
+ self._goal_pub.publish(goal)
+
+ def __mean_square_dist_(self, x, y):
+ return math.sqrt(math.pow(x, 2) + math.pow(y, 2))
diff --git a/task_generator/task_generator/marl_tasks.py b/task_generator/task_generator/marl_tasks.py
new file mode 100644
index 000000000..14989314d
--- /dev/null
+++ b/task_generator/task_generator/marl_tasks.py
@@ -0,0 +1,309 @@
+from typing import Type, Union, List
+
+import json
+import os
+import rospkg
+import rospy
+import yaml
+
+from abc import ABC, abstractmethod
+from enum import Enum, unique
+from threading import Lock
+from filelock import FileLock
+
+from nav_msgs.msg import OccupancyGrid
+from nav_msgs.srv import GetMap
+from std_msgs.msg import Bool
+
+from .obstacles_manager import ObstaclesManager
+from .marl_robot_manager import RobotManager
+
+
+class ABSMARLTask(ABC):
+ """An abstract class for the DRL agent navigation tasks."""
+
+ def __init__(
+ self,
+ obstacles_manager: ObstaclesManager,
+ robot_manager: List[RobotManager],
+ ):
+ self.obstacles_manager = obstacles_manager
+ self.robot_manager = robot_manager
+ self._service_client_get_map = rospy.ServiceProxy("/static_map", GetMap)
+ self._map_lock = Lock()
+ rospy.Subscriber("/map", OccupancyGrid, self._update_map)
+ # a mutex keep the map is not unchanged during reset task.
+
+ @abstractmethod
+ def reset(self):
+ """
+ Funciton to reset the task/scenery. Make sure that _map_lock is used.
+ """
+
+ def _update_map(self, map_: OccupancyGrid):
+ with self._map_lock:
+ self.obstacles_manager.update_map(map_)
+ for manager in self.robot_manager:
+ manager.update_map(map_)
+
+
+class RandomMARLTask(ABSMARLTask):
+ """Sets a randomly drawn start and goal position for each robot episodically."""
+
+ def __init__(
+ self,
+ obstacles_manager: ObstaclesManager,
+ robot_manager: List[RobotManager],
+ ):
+ super().__init__(obstacles_manager, robot_manager)
+ self._num_robots = len(self.robot_manager)
+
+ def reset(self):
+ """[summary]"""
+ with self._map_lock:
+ max_fail_times = 3
+ fail_times = 0
+ while fail_times < max_fail_times:
+ try:
+ starts, goals = [None] * self._num_robots, [None] * self._num_robots
+ for i, manager in enumerate(self.robot_manager):
+ start_pos, goal_pos = manager.set_start_pos_goal_pos(
+ forbidden_zones=starts
+ )
+ starts[i] = (
+ start_pos.x,
+ start_pos.y,
+ manager.ROBOT_RADIUS * 1.5,
+ )
+ goals[i] = (
+ goal_pos.x,
+ goal_pos.y,
+ manager.ROBOT_RADIUS * 1.5,
+ )
+ self.obstacles_manager.reset_pos_obstacles_random(
+ forbidden_zones=starts + goals
+ )
+ break
+ except rospy.ServiceException as e:
+ rospy.logwarn(repr(e))
+ fail_times += 1
+ if fail_times == max_fail_times:
+ raise Exception("reset error!")
+
+
+class StagedMARLRandomTask(RandomMARLTask):
+ """
+ Enforces the paradigm of curriculum learning.
+ The training stages are defined in 'training_curriculum.yaml'
+ """
+
+ def __init__(
+ self,
+ ns: str,
+ obstacles_manager: ObstaclesManager,
+ robot_manager: List[RobotManager],
+ start_stage: int = 1,
+ PATHS=None,
+ ) -> None:
+ super().__init__(obstacles_manager, robot_manager)
+ self.ns = ns
+ self.ns_prefix = f"/{ns}/" if ns else ""
+
+ self._curr_stage = start_stage
+ self._stages = {}
+ self._PATHS = PATHS
+ self._read_stages_from_yaml()
+
+ # check start stage format
+ if not isinstance(start_stage, int):
+ raise ValueError("Given start_stage not an Integer!")
+ if self._curr_stage < 1 or self._curr_stage > len(self._stages):
+ raise IndexError(
+ "Start stage given for training curriculum out of bounds! Has to be between {1 to %d}!"
+ % len(self._stages)
+ )
+ rospy.set_param("/curr_stage", self._curr_stage)
+
+ # hyperparamters.json location
+ self.json_file = os.path.join(self._PATHS.get("model"), "hyperparameters.json")
+ if not rospy.get_param("debug_mode"):
+ assert os.path.isfile(self.json_file), (
+ "Found no 'hyperparameters.json' at %s" % self.json_file
+ )
+
+ self._lock_json = FileLock(f"{self.json_file}.lock")
+
+ # subs for triggers
+ self._sub_next = rospy.Subscriber(
+ f"{self.ns_prefix}next_stage", Bool, self.next_stage
+ )
+ self._sub_previous = rospy.Subscriber(
+ f"{self.ns_prefix}previous_stage", Bool, self.previous_stage
+ )
+
+ self._initiate_stage()
+
+ def next_stage(self, *args, **kwargs):
+ if self._curr_stage < len(self._stages):
+ self._curr_stage = self._curr_stage + 1
+ self._initiate_stage()
+
+ if self.ns == "eval_sim":
+ rospy.set_param("/curr_stage", self._curr_stage)
+ if not rospy.get_param("debug_mode"):
+ with self._lock_json:
+ self._update_curr_stage_json()
+
+ if self._curr_stage == len(self._stages):
+ rospy.set_param("/last_stage_reached", True)
+ else:
+ print(
+ f"({self.ns}) INFO: Tried to trigger next stage but already reached last one"
+ )
+
+ def previous_stage(self, *args, **kwargs):
+ if self._curr_stage > 1:
+ rospy.set_param("/last_stage_reached", False)
+
+ self._curr_stage = self._curr_stage - 1
+ self._initiate_stage()
+
+ if self.ns == "eval_sim":
+ rospy.set_param("/curr_stage", self._curr_stage)
+ with self._lock_json:
+ self._update_curr_stage_json()
+ else:
+ print(
+ f"({self.ns}) INFO: Tried to trigger previous stage but already reached first one"
+ )
+
+ def _initiate_stage(self):
+ self._remove_obstacles()
+
+ static_obstacles = self._stages[self._curr_stage]["static"]
+ dynamic_obstacles = self._stages[self._curr_stage]["dynamic"]
+
+ self.obstacles_manager.register_random_static_obstacles(
+ self._stages[self._curr_stage]["static"]
+ )
+ self.obstacles_manager.register_random_dynamic_obstacles(
+ self._stages[self._curr_stage]["dynamic"]
+ )
+
+ print(
+ f"({self.ns}) Stage {self._curr_stage}:"
+ f"Spawning {static_obstacles} static and {dynamic_obstacles} dynamic obstacles!"
+ )
+
+ def _read_stages_from_yaml(self):
+ file_location = self._PATHS.get("curriculum")
+ if os.path.isfile(file_location):
+ with open(file_location, "r") as file:
+ self._stages = yaml.load(file, Loader=yaml.FullLoader)
+ assert isinstance(
+ self._stages, dict
+ ), "'training_curriculum.yaml' has wrong fromat! Has to encode dictionary!"
+ else:
+ raise FileNotFoundError(
+ "Couldn't find 'training_curriculum.yaml' in %s "
+ % self._PATHS.get("curriculum")
+ )
+
+ def _update_curr_stage_json(self):
+ with open(self.json_file, "r") as file:
+ hyperparams = json.load(file)
+ try:
+ hyperparams["curr_stage"] = self._curr_stage
+ except Exception as e:
+ raise Warning(
+ f" {e} \n Parameter 'curr_stage' not found in 'hyperparameters.json'!"
+ )
+ else:
+ with open(self.json_file, "w", encoding="utf-8") as target:
+ json.dump(hyperparams, target, ensure_ascii=False, indent=4)
+
+ def _remove_obstacles(self):
+ self.obstacles_manager.remove_obstacles()
+
+
+@unique
+class ARENA_TASKS(Enum):
+ MANUAL = "manual"
+ RANDOM = "random"
+ STAGED = "staged"
+ SCENARIO = "scenario"
+
+
+def get_mode(mode: str) -> ARENA_TASKS:
+ return ARENA_TASKS(mode)
+
+
+def get_MARL_task(
+ ns: str,
+ mode: str,
+ robot_ids: List[str],
+ PATHS: dict,
+ start_stage: int = 1,
+) -> ABSMARLTask:
+ """Function to return desired navigation task manager.
+
+ Args:
+ ns (str): Environments' ROS namespace. There should only be one env per ns.
+ mode (str): avigation task mode for the agents. Modes to chose from: ['random', 'staged']. \
+ Defaults to "random".
+ robot_ids (List[str]): List containing all robots' names in order to address the right namespaces.
+ start_stage (int, optional): Starting difficulty level for the learning curriculum. Defaults to 1.
+ PATHS (dict, optional): Dictionary containing program related paths. Defaults to None.
+
+ Raises:
+ NotImplementedError: The manual task mode is currently not implemented.
+ NotImplementedError: The scenario task mode is currently not implemented.
+
+ Returns:
+ ABSMARLTask: A task manager instance.
+ """
+ assert type(robot_ids) is list
+
+ task_mode = get_mode(mode)
+
+ # get the map
+ service_client_get_map = rospy.ServiceProxy("/static_map", GetMap)
+ map_response = service_client_get_map()
+
+ # use rospkg to get the path where the model config yaml file stored
+ models_folder_path = rospkg.RosPack().get_path("simulator_setup")
+
+ # robot's yaml file is needed to get its configurations etc.
+ robot_model = rospy.get_param("model")
+ base_robot_yaml = os.path.join(
+ models_folder_path, "robot", f"{robot_model}.model.yaml"
+ )
+
+ robot_manager = [
+ RobotManager(
+ ns=ns,
+ map_=map_response.map,
+ robot_yaml_path=base_robot_yaml,
+ robot_type=robot_model,
+ robot_id=name,
+ )
+ for name in robot_ids
+ ]
+
+ obstacles_manager = ObstaclesManager(ns, map_response.map)
+
+ task = None
+ if task_mode == ARENA_TASKS.MANUAL:
+ raise NotImplementedError
+ if task_mode == ARENA_TASKS.RANDOM:
+ rospy.set_param("/task_mode", "random")
+ obstacles_manager.register_random_obstacles(10, 1.0)
+ task = RandomMARLTask(obstacles_manager, robot_manager)
+ if task_mode == ARENA_TASKS.STAGED:
+ rospy.set_param("/task_mode", "staged")
+ task = StagedMARLRandomTask(
+ ns, obstacles_manager, robot_manager, start_stage, PATHS
+ )
+ if task_mode == ARENA_TASKS.SCENARIO:
+ raise NotImplementedError
+ return task
diff --git a/task_generator/task_generator/robot_manager.py b/task_generator/task_generator/robot_manager.py
index 89f9ef0db..97baca81e 100755
--- a/task_generator/task_generator/robot_manager.py
+++ b/task_generator/task_generator/robot_manager.py
@@ -1,5 +1,6 @@
# from math import ceil, sqrt
import math
+import rospkg
import yaml
import os
import threading
@@ -26,9 +27,7 @@ class RobotManager:
is managed
"""
- def __init__(
- self, ns: str, map_: OccupancyGrid, robot_yaml_path: str, timeout=20
- ):
+ def __init__(self, ns: str, map_: OccupancyGrid, robot_yaml_path: str, timeout=20):
"""[summary]
Args:
@@ -54,9 +53,7 @@ def __init__(
f"{self.ns_prefix}spawn_model", SpawnModel
)
# it's only needed in training mode to send the clock signal.
- self._step_world = rospy.ServiceProxy(
- f"{self.ns_prefix}step_world", StepWorld
- )
+ self._step_world = rospy.ServiceProxy(f"{self.ns_prefix}step_world", StepWorld)
# publisher
# publish the start position of the robot
@@ -99,7 +96,18 @@ def _get_robot_configration(self, robot_yaml_path):
robot_yaml_path ([type]): [description]
"""
self.ROBOT_NAME = os.path.basename(robot_yaml_path).split(".")[0]
- self.ROBOT_RADIUS = rospy.get_param("radius")
+
+ robot_config = os.path.join(
+ rospkg.RosPack().get_path("arena_local_planner_drl"),
+ "configs",
+ "action_spaces",
+ "default_settings_" + self.ROBOT_NAME + ".yaml",
+ )
+
+ with open(robot_config, "r", encoding="utf-8") as target:
+ config = yaml.load(target, Loader=yaml.FullLoader)
+ self.ROBOT_RADIUS = config["robot"]["radius"]
+
with open(robot_yaml_path, "r") as f:
robot_data = yaml.safe_load(f)
@@ -134,9 +142,7 @@ def move_robot(self, pose: Pose2D):
# assert self.step_size * \
# self.LASER_UPDATE_RATE == 1, f"TO run the traning successfully, make sure the laser_update_rate*step_size == 1 \
# \n\tcurrent step_size:\t {self.step_size}\n\tcurrent laser's update rate:\t {self.LASER_UPDATE_RATE} "
- for _ in range(
- math.ceil(1 / (self.step_size * self.LASER_UPDATE_RATE))
- ):
+ for _ in range(math.ceil(1 / (self.step_size * self.LASER_UPDATE_RATE))):
self._step_world()
def set_start_pos_random(self):
@@ -179,31 +185,20 @@ def dist(x1, y1, x2, y2):
if start_pos is None:
start_pos_ = Pose2D()
- (
- start_pos_.x,
- start_pos_.y,
- start_pos_.theta,
- ) = get_random_pos_on_map(
+ (start_pos_.x, start_pos_.y, start_pos_.theta,) = get_random_pos_on_map(
self._free_space_indices, self.map, self.ROBOT_RADIUS * 2
)
else:
start_pos_ = start_pos
if goal_pos is None:
goal_pos_ = Pose2D()
- (
- goal_pos_.x,
- goal_pos_.y,
- goal_pos_.theta,
- ) = get_random_pos_on_map(
+ (goal_pos_.x, goal_pos_.y, goal_pos_.theta,) = get_random_pos_on_map(
self._free_space_indices, self.map, self.ROBOT_RADIUS * 2
)
else:
goal_pos_ = goal_pos
- if (
- dist(start_pos_.x, start_pos_.y, goal_pos_.x, goal_pos_.y)
- < min_dist
- ):
+ if dist(start_pos_.x, start_pos_.y, goal_pos_.x, goal_pos_.y) < min_dist:
i_try += 1
continue
# move the robot to the start pos
diff --git a/task_generator/task_generator/utils.py b/task_generator/task_generator/utils.py
index 9653e8af0..eac16f5d9 100755
--- a/task_generator/task_generator/utils.py
+++ b/task_generator/task_generator/utils.py
@@ -30,7 +30,11 @@ def get_random_pos_on_map(free_space_indices, map_: OccupancyGrid, safe_dist: fl
def is_pos_valid(x_in_meters, y_in_meters):
for forbidden_zone in forbidden_zones:
- if (x_in_meters-forbidden_zone[0])**2+(y_in_meters-forbidden_zone[1])**2 < (forbidden_zone[2]+safe_dist)**2:
+ if (
+ forbidden_zone
+ and (x_in_meters - forbidden_zone[0]) ** 2 + (y_in_meters - forbidden_zone[1]) ** 2
+ < (forbidden_zone[2] + safe_dist) ** 2
+ ):
return False
# in pixel
@@ -48,8 +52,7 @@ def is_pos_valid(x_in_meters, y_in_meters):
try:
value = map_.data[index]
except IndexError:
- print("IndexError: index: %d, map_length: %d" %
- (index, len(map_.data)))
+ print("IndexError: index: %d, map_length: %d" % (index, len(map_.data)))
return False
if value != 0:
@@ -57,7 +60,8 @@ def is_pos_valid(x_in_meters, y_in_meters):
return True
assert len(free_space_indices) == 2 and len(free_space_indices[0]) == len(
- free_space_indices[1]), "free_space_indices is not correctly setup"
+ free_space_indices[1]
+ ), "free_space_indices is not correctly setup"
if forbidden_zones is None:
forbidden_zones = []
@@ -66,7 +70,7 @@ def is_pos_valid(x_in_meters, y_in_meters):
n_check_failed = 0
x_in_meters, y_in_meters = None, None
while not pos_valid:
- idx = random.randint(0, n_freespace_cells-1)
+ idx = random.randint(0, n_freespace_cells - 1)
# in cells
y_in_cells, x_in_cells = free_space_indices[0][idx], free_space_indices[1][idx]
# convert x, y in meters
@@ -76,8 +80,7 @@ def is_pos_valid(x_in_meters, y_in_meters):
if not pos_valid:
n_check_failed += 1
if n_check_failed > 100:
- raise Exception(
- "cann't find any no-occupied space please check the map information")
+ raise Exception("cann't find any no-occupied space please check the map information")
# in radius
theta = random.uniform(-math.pi, math.pi)