Refactor TD-MPC (#103)

Co-authored-by: Cadene <re.cadene@gmail.com>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -43,15 +43,16 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
     def __init__(
         self,
         config: DiffusionConfig | None = None,
-        dataset_stats=None,
+        dataset_stats: dict[str, dict[str, Tensor]] | None = None,
     ):
         """
         Args:
             config: Policy configuration class instance or None, in which case the default instantiation of
-                    the configuration class is used.
+                the configuration class is used.
+            dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected
+                that they will be passed with a call to `load_state_dict` before the policy is used.
         """
         super().__init__()
-        # TODO(alexander-soare): LR scheduler will be removed.
         if config is None:
             config = DiffusionConfig()
         self.config = config
@@ -88,7 +89,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
         }
 
     @torch.no_grad
-    def select_action(self, batch: dict[str, Tensor], **_) -> Tensor:
+    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Select a single action given environment observations.
 
         This method handles caching a history of observations and an action trajectory generated by the
@@ -136,7 +137,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
         action = self._queues["action"].popleft()
         return action
 
-    def forward(self, batch: dict[str, Tensor], **_) -> dict[str, Tensor]:
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
         batch = self.normalize_targets(batch)