Refactor SAC configuration and policy for improved action sampling and stability

- Updated SACConfig to replace standard deviation parameterization with log_std_min and log_std_max for better control over action distributions.
- Modified SACPolicy to streamline action selection and log probability calculations, enhancing stochastic behavior.
- Removed deprecated TanhMultivariateNormalDiag class to simplify the codebase and improve maintainability.

These changes aim to enhance the robustness and performance of the SAC implementation during training and inference.

lerobot/common/policies/sac/configuration_sac.py

@@ -53,30 +53,13 @@ class SACConfig:
     critic_network_kwargs = {
         "hidden_dims": [256, 256],
         "activate_final": True,
-    }
+        }
     actor_network_kwargs = {
         "hidden_dims": [256, 256],
         "activate_final": True,
-    }
+        }
     policy_kwargs = {
-            "tanh_squash_distribution": True,
-            "std_parameterization": "softplus",
-            "std_min": 0.005,
-            "std_max": 5.0,
+        "use_tanh_squash": True,
+        "log_std_min": -5,
+        "log_std_max": 2,
         }
-    )
-    output_shapes: dict[str, list[int]] = field(
-        default_factory=lambda: {
-            "action": [4],
-        }
-    )
-
-    state_encoder_hidden_dim: int = 256
-    latent_dim: int = 256
-    network_hidden_dims: int = 256
-
-    # Normalization / Unnormalization
-    input_normalization_modes: dict[str, str] | None = None
-    output_normalization_modes: dict[str, str] = field(
-        default_factory=lambda: {"action": "min_max"},
-    )