- added CrossQ support for MultiInputPolicy

Author: RaikoPipe

sb3_contrib/crossq/crossq.py

@@ -10,7 +10,7 @@
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
 from torch.nn import functional as F
 
-from sb3_contrib.crossq.policies import Actor, CrossQCritic, CrossQPolicy, MlpPolicy
+from sb3_contrib.crossq.policies import Actor, CrossQCritic, CrossQPolicy, MlpPolicy, MultiInputPolicy
 
 SelfCrossQ = TypeVar("SelfCrossQ", bound="CrossQ")
 
@@ -67,6 +67,7 @@ class CrossQ(OffPolicyAlgorithm):
 
     policy_aliases: ClassVar[dict[str, type[BasePolicy]]] = {
         "MlpPolicy": MlpPolicy,
+        "MultiInputPolicy": MultiInputPolicy,
         # TODO: Implement CnnPolicy and MultiInputPolicy
     }
     policy: CrossQPolicy
@@ -235,7 +236,14 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             #
             # 2. From a computational perspective a single forward pass is simply more efficient than
             #    two sequential forward passes.
-            all_obs = th.cat([replay_data.observations, replay_data.next_observations], dim=0)
+
+            if isinstance(replay_data.observations, dict):
+                all_obs = {
+                    key: th.cat([replay_data.observations[key], replay_data.next_observations[key]], dim=0)
+                    for key in replay_data.observations.keys()
+                }
+            else:
+                all_obs = th.cat([replay_data.observations, replay_data.next_observations], dim=0)
             all_actions = th.cat([replay_data.actions, next_actions], dim=0)
             # Update critic BN stats
             self.critic.set_bn_training_mode(True)
@@ -331,3 +339,4 @@ def _get_torch_save_params(self) -> tuple[list[str], list[str]]:
         else:
             saved_pytorch_variables = ["ent_coef_tensor"]
         return state_dicts, saved_pytorch_variables
+

sb3_contrib/crossq/policies.py

@@ -9,6 +9,7 @@
 from stable_baselines3.common.torch_layers import (
     BaseFeaturesExtractor,
     FlattenExtractor,
+    CombinedExtractor,
     create_mlp,
     get_actor_critic_arch,
 )
@@ -529,3 +530,77 @@ def set_training_mode(self, mode: bool) -> None:
 
 
 MlpPolicy = CrossQPolicy
+
+class MultiInputPolicy(CrossQPolicy):
+    """
+    Policy class (with both actor and critic) for CrossQ.
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param use_sde: Whether to use State Dependent Exploration or not
+    :param log_std_init: Initial value for the log standard deviation
+    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
+        a positive standard deviation (cf paper). It allows to keep variance
+        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
+    :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
+    :param features_extractor_class: Features extractor to use.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    :param n_quantiles: Number of quantiles for the critic.
+    :param n_critics: Number of critic networks to create.
+    :param share_features_extractor: Whether to share or not the features extractor
+        between the actor and the critic (this saves computation time)
+    """
+
+    def __init__(
+        self,
+        observation_space: spaces.Space,
+        action_space: spaces.Box,
+        lr_schedule: Schedule,
+        net_arch: Optional[Union[list[int], dict[str, list[int]]]] = None,
+        activation_fn: type[nn.Module] = nn.ReLU,
+        batch_norm: bool = True,
+        batch_norm_momentum: float = 0.01,  # Note: Jax implementation is 1 - momentum = 0.99
+        batch_norm_eps: float = 0.001,
+        renorm_warmup_steps: int = 100_000,
+        use_sde: bool = False,
+        log_std_init: float = -3,
+        use_expln: bool = False,
+        clip_mean: float = 2.0,
+        features_extractor_class: type[BaseFeaturesExtractor] = CombinedExtractor,
+        features_extractor_kwargs: Optional[dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[dict[str, Any]] = None,
+        n_critics: int = 2,
+        share_features_extractor: bool = False,
+    ):
+        super().__init__(
+            observation_space,
+            action_space,
+            lr_schedule,
+            net_arch,
+            activation_fn,
+            batch_norm,
+            batch_norm_momentum,
+            batch_norm_eps,
+            renorm_warmup_steps,
+            use_sde,
+            log_std_init,
+            use_expln,
+            clip_mean,
+            features_extractor_class,
+            features_extractor_kwargs,
+            normalize_images,
+            optimizer_class,
+            optimizer_kwargs,
+            n_critics,
+            share_features_extractor,
+        )