Refactor SACPolicy for improved type annotations and readability

- Enhanced type annotations for variables in the `SACPolicy` class to improve code clarity.
- Updated method calls to use keyword arguments for better readability.
- Streamlined the extraction of batch components, ensuring consistent typing across the class methods.

lerobot/common/policies/sac/modeling_sac.py

@@ -200,16 +200,16 @@ class SACPolicy(
         """
         # TODO: (maractingi, azouitine) Respect the function signature we output tensors
         # Extract common components from batch
-        actions = batch["action"]
+        actions: Tensor = batch["action"]
-        observations = batch["state"]
+        observations: dict[str, Tensor] = batch["state"]
-        observation_features = batch.get("observation_feature")
+        observation_features: Tensor = batch.get("observation_feature")
 
         if model == "critic":
             # Extract critic-specific components
-            rewards = batch["reward"]
+            rewards: Tensor = batch["reward"]
-            next_observations = batch["next_state"]
+            next_observations: dict[str, Tensor] = batch["next_state"]
-            done = batch["done"]
+            done: Tensor = batch["done"]
-            next_observation_features = batch.get("next_observation_feature")
+            next_observation_features: Tensor = batch.get("next_observation_feature")
 
             return self.compute_loss_critic(
                 observations=observations,
@@ -287,8 +287,8 @@ class SACPolicy(
 
         # 3- compute predicted qs
         q_preds = self.critic_forward(
-            observations,
+            observations=observations,
-            actions,
+            actions=actions,
             use_target=False,
             observation_features=observation_features,
         )
@@ -302,7 +302,7 @@ class SACPolicy(
                 input=q_preds,
                 target=td_target_duplicate,
                 reduction="none",
-            ).mean(1)
+            ).mean(dim=1)
         ).sum()
         return critics_loss
 
@@ -324,11 +324,11 @@ class SACPolicy(
         actions_pi, log_probs, _ = self.actor(observations, observation_features)
 
         # TODO: (maractingi, azouitine) This is to slow, we should find a way to do this in a more efficient way
-        actions_pi = self.unnormalize_outputs({"action": actions_pi})["action"]
+        actions_pi: Tensor = self.unnormalize_outputs({"action": actions_pi})["action"]
 
         q_preds = self.critic_forward(
-            observations,
+            observations=observations,
-            actions_pi,
+            actions=actions_pi,
             use_target=False,
             observation_features=observation_features,
         )