List -> list

cares_reinforcement_learning/util/configurations.py

@@ -1,4 +1,4 @@
-from typing import List, Optional
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -10,7 +10,7 @@
 
 # pylint disbale-next=unused-import
 
-# NOTE: If a parameter is a list then don't wrap with Optional leave as implicit optional - List[type] = default
+# NOTE: If a parameter is a list then don't wrap with Optional leave as implicit optional - list[type] = default
 
 
 class SubscriptableClass(BaseModel):
@@ -27,14 +27,14 @@ class TrainingConfig(SubscriptableClass):
     Configuration class for training.
 
     Attributes:
-        seeds (List[int]): List of random seeds for reproducibility. Default is [10].
+        seeds (list[int]): list of random seeds for reproducibility. Default is [10].
         plot_frequency (Optional[int]): Frequency at which to plot training progress. Default is 100.
         checkpoint_frequency (Optional[int]): Frequency at which to save model checkpoints. Default is 100.
         number_steps_per_evaluation (Optional[int]): Number of steps per evaluation. Default is 10000.
         number_eval_episodes (Optional[int]): Number of episodes to evaluate during training. Default is 10.
     """
 
-    seeds: List[int] = [10]
+    seeds: list[int] = [10]
     plot_frequency: Optional[int] = 100
     checkpoint_frequency: Optional[int] = 100
     number_steps_per_evaluation: Optional[int] = 10000
@@ -63,7 +63,7 @@ class AlgorithmConfig(SubscriptableClass):
 
         image_observation (Optional[int]): Whether the observation is an image.
 
-        hidden_size (List[int]): List of hidden layer sizes - e.g. [256, 256].
+        hidden_size (list[int]): list of hidden layer sizes - e.g. [256, 256].
     """
 
     algorithm: str = Field(description="Name of the algorithm to be used")
@@ -81,7 +81,7 @@ class AlgorithmConfig(SubscriptableClass):
 
     image_observation: Optional[int] = 0
 
-    hidden_size: List[int] = None
+    hidden_size: list[int] | None = None
 
 
 class DQNConfig(AlgorithmConfig):
@@ -181,7 +181,7 @@ class SACConfig(AlgorithmConfig):
     tau: Optional[float] = 0.005
     reward_scale: Optional[float] = 1.0
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]
 
 
 class SACAEConfig(AlgorithmConfig):
@@ -198,7 +198,7 @@ class SACAEConfig(AlgorithmConfig):
     tau: Optional[float] = 0.005
     reward_scale: Optional[float] = 1.0
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]
 
     encoder_tau: Optional[float] = 0.05
     decoder_update_freq: Optional[int] = 1
@@ -248,7 +248,7 @@ class DynaSACConfig(AlgorithmConfig):
     gamma: Optional[float] = 0.99
     tau: Optional[float] = 0.005
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]
 
     horizon: Optional[int] = 3
     num_samples: Optional[int] = 10
@@ -318,7 +318,7 @@ class TQCConfig(AlgorithmConfig):
     num_quantiles: Optional[int] = 25
     num_nets: Optional[int] = 5
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]
 
 
 class CTD4Config(AlgorithmConfig):
@@ -362,7 +362,7 @@ class PERSACConfig(AlgorithmConfig):
     per_alpha: Optional[float] = 0.6
     min_priority: Optional[float] = 1e-6
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]
 
 
 class LAPTD3Config(AlgorithmConfig):
@@ -391,7 +391,7 @@ class LAPSACConfig(AlgorithmConfig):
     reward_scale: Optional[float] = 1.0
     min_priority: Optional[float] = 1.0
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]
 
 
 class PALTD3Config(AlgorithmConfig):
@@ -436,7 +436,7 @@ class LA3PSACConfig(AlgorithmConfig):
     min_priority: Optional[float] = 1.0
     prioritized_fraction: Optional[float] = 0.5
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]
 
 
 class MAPERTD3Config(AlgorithmConfig):
@@ -477,8 +477,8 @@ class MAPERSACConfig(AlgorithmConfig):
     G: Optional[int] = 64
     number_steps_per_train_policy: Optional[int] = 64
 
-    hidden_size: List[int] = [400, 300]
-    log_std_bounds: List[float] = [-20, 2]
+    hidden_size: list[int] = [400, 300]
+    log_std_bounds: list[float] = [-20, 2]
 
 
 class RDTD3Config(AlgorithmConfig):
@@ -506,4 +506,4 @@ class RDSACConfig(AlgorithmConfig):
     per_alpha: Optional[float] = 0.7
     min_priority: Optional[float] = 1.0
 
-    log_std_bounds: List[float] = [-20, 2]
+    log_std_bounds: list[float] = [-20, 2]