fix(sac): make temperature a property to fix checkpoint resume bug (#2877)

* fix(sac): make temperature a property to fix checkpoint resume bug

Temperature was stored as a plain float and not restored after loading
a checkpoint, causing incorrect loss computations until update_temperature()
was called. Changed to a property that always computes from log_alpha,
ensuring correct behavior after checkpoint loading.

* simplify docstrings

src/lerobot/policies/sac/modeling_sac.py

@@ -239,8 +239,10 @@ class SACPolicy(
                     + target_param.data * (1.0 - self.config.critic_target_update_weight)
                 )
 
-    def update_temperature(self):
+    @property
-        self.temperature = self.log_alpha.exp().item()
+    def temperature(self) -> float:
+        """Return the current temperature value, always in sync with log_alpha."""
+        return self.log_alpha.exp().item()
 
     def compute_loss_critic(
         self,
@@ -457,11 +459,10 @@ class SACPolicy(
             dim = continuous_action_dim + (1 if self.config.num_discrete_actions is not None else 0)
             self.target_entropy = -np.prod(dim) / 2
 
-    def _init_temperature(self):
+    def _init_temperature(self) -> None:
-        """Set up temperature parameter and initial log_alpha."""
+        """Set up temperature parameter (log_alpha)."""
         temp_init = self.config.temperature_init
         self.log_alpha = nn.Parameter(torch.tensor([math.log(temp_init)]))
-        self.temperature = self.log_alpha.exp().item()
 
 
 class SACObservationEncoder(nn.Module):

src/lerobot/rl/learner.py

@@ -545,9 +545,6 @@ def add_actor_information_and_train(
                 training_infos["temperature_grad_norm"] = temp_grad_norm
                 training_infos["temperature"] = policy.temperature
 
-                # Update temperature
-                policy.update_temperature()
-
         # Push policy to actors if needed
         if time.time() - last_time_policy_pushed > policy_parameters_push_frequency:
             push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)

tests/policies/test_sac_policy.py

@@ -441,12 +441,13 @@ def test_sac_policy_with_predefined_entropy():
 
 
 def test_sac_policy_update_temperature():
+    """Test that temperature property is always in sync with log_alpha."""
     config = create_default_config(continuous_action_dim=10, state_dim=10)
     policy = SACPolicy(config=config)
 
     assert policy.temperature == pytest.approx(1.0)
     policy.log_alpha.data = torch.tensor([math.log(0.1)])
-    policy.update_temperature()
+    # Temperature property automatically reflects log_alpha changes
     assert policy.temperature == pytest.approx(0.1)