SAC works

lerobot/common/policies/sac/modeling_sac.py

@@ -271,6 +271,9 @@ class SACPolicy(
             q_targets = self.critic_forward(
                 observations=next_observations, actions=next_action_preds, use_target=True
             )
+            q_targets = self.critic_forward(
+                observations=next_observations, actions=next_action_preds, use_target=True
+            )
 
             # subsample critics to prevent overfitting if use high UTD (update to date)
             if self.config.num_subsample_critics is not None:

lerobot/scripts/train_sac.py

@@ -265,6 +265,9 @@ class ReplayBuffer:
         batch_dones = torch.tensor([t["done"] for t in list_of_transitions], dtype=torch.float32).to(
             self.device
         )
+        batch_dones = torch.tensor([t["done"] for t in list_of_transitions], dtype=torch.float32).to(
+            self.device
+        )
 
         # Return a BatchTransition typed dict
         return BatchTransition(