added optimizer and sac to factory.py

lerobot/common/policies/factory.py

@@ -66,6 +66,12 @@ def get_policy_and_config_classes(name: str) -> tuple[Policy, object]:
         from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTPolicy
 
         return VQBeTPolicy, VQBeTConfig
+    elif name == "sac":
+        from lerobot.common.policies.sac.configuration_sac import SACConfig
+        from lerobot.common.policies.sac.modeling_sac import SACPolicy
+
+        return SACPolicy, SACConfig
+
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 

lerobot/common/policies/sac/configuration_sac.py

@@ -26,6 +26,7 @@ class SACConfig:
     num_subsample_critics = None
     critic_lr = 3e-4
     actor_lr = 3e-4
+    temperature_lr = 3e-4
     critic_target_update_weight = 0.005
     utd_ratio = 2
     critic_network_kwargs = {

lerobot/scripts/train.py

@@ -93,6 +93,15 @@ def make_optimizer_and_scheduler(cfg, policy):
     elif policy.name == "tdmpc":
         optimizer = torch.optim.Adam(policy.parameters(), cfg.training.lr)
         lr_scheduler = None
+
+    elif policy.name == "sac":
+        optimizer = torch.optim.Adam([
+			{'params': policy.actor.parameters(), 'lr': policy.config.actor_lr},
+            {'params': policy.critic_ensemble.parameters(), 'lr': policy.config.critic_lr},
+			{'params': policy.temperature.parameters(), 'lr': policy.config.temperature_lr},
+            ])
+        lr_scheduler = None		
+
     elif cfg.policy.name == "vqbet":
         from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTOptimizer, VQBeTScheduler