Update

[ghstack-poisoned]

.github/unittest/linux/scripts/environment.yml

@@ -9,7 +9,7 @@ dependencies:
     - future
     - cloudpickle
     - pygame
-    - moviepy
+    - moviepy<2.0.0
     - tqdm
     - pytest
     - pytest-cov

.github/unittest/linux_distributed/scripts/environment.yml

@@ -9,7 +9,7 @@ dependencies:
     - future
     - cloudpickle
     - pygame
-    - moviepy
+    - moviepy<2.0.0
     - tqdm
     - pytest
     - pytest-cov

.github/unittest/linux_libs/scripts_envpool/environment.yml

@@ -9,7 +9,7 @@ dependencies:
     - future
     - cloudpickle
     - pygame
-    - moviepy
+    - moviepy<2.0.0
     - pytest-cov
     - pytest-mock
     - pytest-instafail

.github/unittest/linux_libs/scripts_gym/environment.yml

@@ -11,7 +11,7 @@ dependencies:
     - future
     - cloudpickle
     - pygame
-    - moviepy
+    - moviepy<2.0.0
     - tqdm
     - pytest
     - pytest-cov

.github/unittest/linux_libs/scripts_robohive/environment.yml

@@ -11,7 +11,7 @@ dependencies:
     - future
     - cloudpickle
     - pygame
-    - moviepy
+    - moviepy<2.0.0
     - tqdm
     - pytest
     - pytest-cov

.github/unittest/linux_olddeps/scripts_gym_0_13/environment.yml

@@ -10,7 +10,7 @@ dependencies:
     - cloudpickle
     - gym[atari]==0.13
     - pygame
-    - moviepy
+    - moviepy<2.0.0
     - tqdm
     - pytest
     - pytest-cov

.github/unittest/linux_sota/scripts/environment.yml

@@ -9,7 +9,7 @@ dependencies:
     - future
     - cloudpickle
     - pygame
-    - moviepy
+    - moviepy<2.0.0
     - tqdm
     - pytest
     - pytest-cov

README.md

@@ -921,7 +921,7 @@ make of torchrl:
 pip3 install tqdm tensorboard "hydra-core>=1.1" hydra-submitit-launcher
 
 # rendering
-pip3 install moviepy
+pip3 install "moviepy<2.0.0"
 
 # deepmind control suite
 pip3 install dm_control

docs/source/reference/modules.rst

@@ -373,6 +373,8 @@ algorithms, such as DQN, DDPG or Dreamer.
     OnlineDTActor
     RSSMPosterior
     RSSMPrior
+    set_recurrent_mode
+    recurrent_mode
 
 Multi-agent-specific modules
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

examples/distributed/collectors/multi_nodes/ray_train.py

@@ -85,8 +85,8 @@
         in_keys=["loc", "scale"],
         distribution_class=TanhNormal,
         distribution_kwargs={
-            "low": env.action_spec.space.low,
-            "high": env.action_spec.space.high,
+            "low": env.action_spec_unbatched.space.low,
+            "high": env.action_spec_unbatched.space.high,
         },
         return_log_prob=True,
     )

setup.py

@@ -208,7 +208,7 @@ def _main(argv):
         ],
         "dm_control": ["dm_control"],
         "gym_continuous": ["gymnasium<1.0", "mujoco"],
-        "rendering": ["moviepy"],
+        "rendering": ["moviepy<2.0.0"],
         "tests": ["pytest", "pyyaml", "pytest-instafail", "scipy"],
         "utils": [
             "tensorboard",

sota-check/README.md

@@ -26,7 +26,7 @@ export MUJOCO_GL=egl
 conda create -n rl-sota-bench python=3.10 -y 
 conda install anaconda::libglu -y
 pip3 install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu121
-pip3 install "gymnasium[accept-rom-license,atari,mujoco]" vmas tqdm wandb pygame moviepy imageio submitit hydra-core transformers
+pip3 install "gymnasium[accept-rom-license,atari,mujoco]" vmas tqdm wandb pygame "moviepy<2.0.0" imageio submitit hydra-core transformers
 
 cd /path/to/tensordict
 python setup.py develop

sota-implementations/a2c/utils_atari.py

@@ -100,8 +100,8 @@ def make_ppo_modules_pixels(proof_environment, device):
         num_outputs = proof_environment.single_action_spec.shape
         distribution_class = TanhNormal
         distribution_kwargs = {
-            "low": proof_environment.single_action_spec.space.low.to(device),
-            "high": proof_environment.single_action_spec.space.high.to(device),
+            "low": proof_environment.action_spec_unbatched.space.low.to(device),
+            "high": proof_environment.action_spec_unbatched.space.high.to(device),
         }
 
     # Define input keys

sota-implementations/a2c/utils_mujoco.py

@@ -56,8 +56,8 @@ def make_ppo_models_state(proof_environment, device, *, compile: bool = False):
     num_outputs = proof_environment.single_action_spec.shape[-1]
     distribution_class = TanhNormal
     distribution_kwargs = {
-        "low": proof_environment.single_action_spec.space.low.to(device),
-        "high": proof_environment.single_action_spec.space.high.to(device),
+        "low": proof_environment.action_spec_unbatched.space.low.to(device),
+        "high": proof_environment.action_spec_unbatched.space.high.to(device),
         "tanh_loc": False,
         "safe_tanh": True,
     }

sota-implementations/cql/utils.py

@@ -202,7 +202,7 @@ def make_offline_replay_buffer(rb_cfg):
 def make_cql_model(cfg, train_env, eval_env, device="cpu"):
     model_cfg = cfg.model
 
-    action_spec = train_env.single_action_spec
+    action_spec = train_env.action_spec_unbatched
 
     actor_net, q_net = make_cql_modules_state(model_cfg, eval_env)
     in_keys = ["observation"]

sota-implementations/crossq/utils.py

@@ -157,7 +157,7 @@ def make_crossQ_agent(cfg, train_env, device):
     """Make CrossQ agent."""
     # Define Actor Network
     in_keys = ["observation"]
-    action_spec = train_env.single_action_spec
+    action_spec = train_env.action_spec_unbatched
     actor_net_kwargs = {
         "num_cells": cfg.network.actor_hidden_sizes,
         "out_features": 2 * action_spec.shape[-1],

sota-implementations/decision_transformer/utils.py

@@ -404,8 +404,10 @@ def make_dt_model(cfg, device: torch.device | None = None):
         make_base_env(env_cfg), env_cfg, obs_loc=0, obs_std=1
     )
 
-    action_spec = proof_environment.single_action_spec
-    obs_spec = proof_environment.single_observation_spec
+    action_spec = proof_environment.action_spec_unbatched
+    for key, value in proof_environment.observation_spec.items():
+        if key == "observation":
+            state_dim = value.shape[-1]
     in_keys = [
         "observation_cat",
         "action_cat",

sota-implementations/dreamer/dreamer.py

@@ -20,7 +20,7 @@
 )
 
 # mixed precision training
-from torch.cuda.amp import GradScaler
+from torch.amp import GradScaler
 from torch.nn.utils import clip_grad_norm_
 from torchrl._utils import logger as torchrl_logger, timeit
 from torchrl.envs.utils import ExplorationType, set_exploration_type
@@ -321,6 +321,14 @@ def compile_rssms(module):
 
         t_collect_init = time.time()
 
+    test_env.close()
+    train_env.close()
+    collector.shutdown()
+
+    del test_env
+    del train_env
+    del collector
+
 
 if __name__ == "__main__":
     main()

sota-implementations/gail/ppo_utils.py

@@ -51,8 +51,8 @@ def make_ppo_models_state(proof_environment):
     num_outputs = proof_environment.single_action_spec.shape[-1]
     distribution_class = TanhNormal
     distribution_kwargs = {
-        "low": proof_environment.single_action_spec.space.low,
-        "high": proof_environment.single_action_spec.space.high,
+        "low": proof_environment.action_spec_unbatched.space.low,
+        "high": proof_environment.action_spec_unbatched.space.high,
         "tanh_loc": False,
     }
 

sota-implementations/iql/utils.py

@@ -195,9 +195,7 @@ def make_iql_model(cfg, train_env, eval_env, device="cpu"):
     model_cfg = cfg.model
 
     in_keys = ["observation"]
-    action_spec = train_env.action_spec
-    if train_env.batch_size:
-        action_spec = action_spec[(0,) * len(train_env.batch_size)]
+    action_spec = train_env.action_spec_unbatched
     actor_net, q_net, value_net = make_iql_modules_state(model_cfg, eval_env)
 
     out_keys = ["loc", "scale"]

sota-implementations/multiagent/README.md

@@ -27,7 +27,7 @@ Install vmas and dependencies:
 
 ```bash
 pip install vmas
-pip install wandb moviepy
+pip install wandb "moviepy<2.0.0"
 pip install hydra-core
 ```
 

sota-implementations/multiagent/iql.py

@@ -72,7 +72,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
     # Policy
     net = MultiAgentMLP(
         n_agent_inputs=env.observation_spec["agents", "observation"].shape[-1],
-        n_agent_outputs=env.action_spec.space.n,
+        n_agent_outputs=env.full_action_spec["agents", "action"].space.n,
         n_agents=env.n_agents,
         centralised=False,
         share_params=cfg.model.shared_parameters,
@@ -91,7 +91,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
             ("agents", "action_value"),
             ("agents", "chosen_action_value"),
         ],
-        spec=env.unbatched_action_spec,
+        spec=env.full_action_spec_unbatched,
         action_space=None,
     )
     qnet = SafeSequential(module, value_module)
@@ -103,7 +103,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
             eps_end=0,
             annealing_num_steps=int(cfg.collector.total_frames * (1 / 2)),
             action_key=env.action_key,
-            spec=env.unbatched_action_spec,
+            spec=env.full_action_spec_unbatched,
         ),
     )
 

sota-implementations/multiagent/maddpg_iddpg.py

@@ -91,21 +91,21 @@ def train(cfg: "DictConfig"):  # noqa: F821
     )
     policy = ProbabilisticActor(
         module=policy_module,
-        spec=env.unbatched_action_spec,
+        spec=env.full_action_spec_unbatched,
         in_keys=[("agents", "param")],
         out_keys=[env.action_key],
         distribution_class=TanhDelta,
         distribution_kwargs={
-            "low": env.unbatched_action_spec[("agents", "action")].space.low,
-            "high": env.unbatched_action_spec[("agents", "action")].space.high,
+            "low": env.full_action_spec_unbatched[("agents", "action")].space.low,
+            "high": env.full_action_spec_unbatched[("agents", "action")].space.high,
         },
         return_log_prob=False,
     )
 
     policy_explore = TensorDictSequential(
         policy,
         AdditiveGaussianModule(
-            spec=env.unbatched_action_spec,
+            spec=env.full_action_spec_unbatched,
             annealing_num_steps=int(cfg.collector.total_frames * (1 / 2)),
             action_key=env.action_key,
             device=cfg.train.device,

sota-implementations/multiagent/mappo_ippo.py

@@ -92,13 +92,13 @@ def train(cfg: "DictConfig"):  # noqa: F821
     )
     policy = ProbabilisticActor(
         module=policy_module,
-        spec=env.unbatched_action_spec,
+        spec=env.full_action_spec_unbatched,
         in_keys=[("agents", "loc"), ("agents", "scale")],
         out_keys=[env.action_key],
         distribution_class=TanhNormal,
         distribution_kwargs={
-            "low": env.unbatched_action_spec[("agents", "action")].space.low,
-            "high": env.unbatched_action_spec[("agents", "action")].space.high,
+            "low": env.full_action_spec_unbatched[("agents", "action")].space.low,
+            "high": env.full_action_spec_unbatched[("agents", "action")].space.high,
         },
         return_log_prob=True,
     )

sota-implementations/multiagent/qmix_vdn.py

@@ -72,7 +72,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
     # Policy
     net = MultiAgentMLP(
         n_agent_inputs=env.observation_spec["agents", "observation"].shape[-1],
-        n_agent_outputs=env.action_spec.space.n,
+        n_agent_outputs=env.full_action_spec["agents", "action"].space.n,
         n_agents=env.n_agents,
         centralised=False,
         share_params=cfg.model.shared_parameters,
@@ -91,7 +91,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
             ("agents", "action_value"),
             ("agents", "chosen_action_value"),
         ],
-        spec=env.unbatched_action_spec,
+        spec=env.full_action_spec_unbatched,
         action_space=None,
     )
     qnet = SafeSequential(module, value_module)
@@ -103,14 +103,14 @@ def train(cfg: "DictConfig"):  # noqa: F821
             eps_end=0,
             annealing_num_steps=int(cfg.collector.total_frames * (1 / 2)),
             action_key=env.action_key,
-            spec=env.unbatched_action_spec,
+            spec=env.full_action_spec_unbatched,
         ),
     )
 
     if cfg.loss.mixer_type == "qmix":
         mixer = TensorDictModule(
             module=QMixer(
-                state_shape=env.unbatched_observation_spec[
+                state_shape=env.observation_spec_unbatched[
                     "agents", "observation"
                 ].shape,
                 mixing_embed_dim=32,

sota-implementations/multiagent/sac.py

@@ -96,13 +96,13 @@ def train(cfg: "DictConfig"):  # noqa: F821
 
         policy = ProbabilisticActor(
             module=policy_module,
-            spec=env.unbatched_action_spec,
+            spec=env.full_action_spec_unbatched,
             in_keys=[("agents", "loc"), ("agents", "scale")],
             out_keys=[env.action_key],
             distribution_class=TanhNormal,
             distribution_kwargs={
-                "low": env.unbatched_action_spec[("agents", "action")].space.low,
-                "high": env.unbatched_action_spec[("agents", "action")].space.high,
+                "low": env.full_action_spec_unbatched[("agents", "action")].space.low,
+                "high": env.full_action_spec_unbatched[("agents", "action")].space.high,
             },
             return_log_prob=True,
         )
@@ -146,7 +146,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
         )
         policy = ProbabilisticActor(
             module=policy_module,
-            spec=env.unbatched_action_spec,
+            spec=env.full_action_spec_unbatched,
             in_keys=[("agents", "logits")],
             out_keys=[env.action_key],
             distribution_class=OneHotCategorical
@@ -194,7 +194,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
             actor_network=policy,
             qvalue_network=value_module,
             delay_qvalue=True,
-            action_spec=env.unbatched_action_spec,
+            action_spec=env.full_action_spec_unbatched,
         )
         loss_module.set_keys(
             state_action_value=("agents", "state_action_value"),
@@ -209,7 +209,7 @@ def train(cfg: "DictConfig"):  # noqa: F821
             qvalue_network=value_module,
             delay_qvalue=True,
             num_actions=env.action_spec.space.n,
-            action_space=env.unbatched_action_spec,
+            action_space=env.full_action_spec_unbatched,
         )
         loss_module.set_keys(
             action_value=("agents", "action_value"),

sota-implementations/ppo/utils_atari.py

@@ -99,8 +99,8 @@ def make_ppo_modules_pixels(proof_environment):
         num_outputs = proof_environment.single_action_spec.shape
         distribution_class = TanhNormal
         distribution_kwargs = {
-            "low": proof_environment.single_action_spec.space.low,
-            "high": proof_environment.single_action_spec.space.high,
+            "low": proof_environment.action_spec_unbatched.space.low,
+            "high": proof_environment.action_spec_unbatched.space.high,
         }
 
     # Define input keys

sota-implementations/ppo/utils_mujoco.py

@@ -51,8 +51,8 @@ def make_ppo_models_state(proof_environment):
     num_outputs = proof_environment.single_action_spec.shape[-1]
     distribution_class = TanhNormal
     distribution_kwargs = {
-        "low": proof_environment.single_action_spec.space.low,
-        "high": proof_environment.single_action_spec.space.high,
+        "low": proof_environment.action_spec_unbatched.space.low,
+        "high": proof_environment.action_spec_unbatched.space.high,
         "tanh_loc": False,
     }
 

sota-implementations/redq/utils.py

@@ -410,7 +410,7 @@ def make_redq_model(
     default_policy_scale = cfg.network.default_policy_scale
     gSDE = cfg.exploration.gSDE
 
-    action_spec = proof_environment.single_action_spec
+    action_spec = proof_environment.action_spec_unbatched
 
     if actor_net_kwargs is None:
         actor_net_kwargs = {}