Cherry-pick BC fixes to Release 10 (#4668)

ml-agents/mlagents/trainers/policy/torch_policy.py

         memories: Optional[torch.Tensor] = None,
         seq_len: int = 1,
         all_log_probs: bool = False,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         :param vec_obs: List of vector observations.
         :param vis_obs: List of visual observations.
         :param all_log_probs: Returns (for discrete actions) a tensor of log probs, one for each action.
-        :return: Tuple of actions, log probabilities (dependent on all_log_probs), entropies, and
-            output memories, all as Torch Tensors.
+        :return: Tuple of actions, actions clipped to -1, 1, log probabilities (dependent on all_log_probs),
+            entropies, and output memories, all as Torch Tensors.
         """
         if memories is None:
             dists, memories = self.actor_critic.get_dists(
             actions = actions[:, 0, :]
         # Use the sum of entropy across actions, not the mean
         entropy_sum = torch.sum(entropies, dim=1)
+
+        if self._clip_action and self.use_continuous_act:
+            clipped_action = torch.clamp(actions, -3, 3) / 3
+        else:
+            clipped_action = actions
+            clipped_action,
             all_logs if all_log_probs else log_probs,
             entropy_sum,
             memories,
 
         run_out = {}
         with torch.no_grad():
-            action, log_probs, entropy, memories = self.sample_actions(
+            action, clipped_action, log_probs, entropy, memories = self.sample_actions(
-        if self._clip_action and self.use_continuous_act:
-            clipped_action = torch.clamp(action, -3, 3) / 3
-        else:
-            clipped_action = action
         run_out["pre_action"] = ModelUtils.to_numpy(action)
         run_out["action"] = ModelUtils.to_numpy(clipped_action)
         # Todo - make pre_action difference

ml-agents/mlagents/trainers/sac/optimizer_torch.py

         self.target_network.network_body.copy_normalization(
             self.policy.actor_critic.network_body
         )
-        (sampled_actions, log_probs, _, _) = self.policy.sample_actions(
+        (sampled_actions, _, log_probs, _, _) = self.policy.sample_actions(
             vec_obs,
             vis_obs,
             masks=act_masks,

ml-agents/mlagents/trainers/tests/torch/saver/test_saver.py

     ).unsqueeze(0)
 
     with torch.no_grad():
-        _, log_probs1, _, _ = policy1.sample_actions(
+        _, _, log_probs1, _, _ = policy1.sample_actions(
-        _, log_probs2, _, _ = policy2.sample_actions(
+        _, _, log_probs2, _, _ = policy2.sample_actions(
             vec_obs, vis_obs, masks=masks, memories=memories, all_log_probs=True
         )
 

ml-agents/mlagents/trainers/tests/torch/test_policy.py

     if len(memories) > 0:
         memories = torch.stack(memories).unsqueeze(0)
 
-    (sampled_actions, log_probs, entropies, memories) = policy.sample_actions(
+    (
+        sampled_actions,
+        clipped_actions,
+        log_probs,
+        entropies,
+        memories,
+    ) = policy.sample_actions(
         vec_obs,
         vis_obs,
         masks=act_masks,
         )
     else:
         assert log_probs.shape == (64, policy.behavior_spec.action_spec.continuous_size)
+        assert clipped_actions.shape == (
+            64,
+            policy.behavior_spec.action_spec.continuous_size,
+        )
     assert entropies.shape == (64,)
 
     if rnn:

ml-agents/mlagents/trainers/torch/components/bc/module.py

         # Don't continue training if the learning rate has reached 0, to reduce training time.
 
         decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
-        if self.current_lr <= 0:
+        if self.current_lr <= 1e-10:  # Unlike in TF, this never actually reaches 0.
             return {"Losses/Pretraining Loss": 0}
 
         batch_losses = []
         else:
             vis_obs = []
 
-        selected_actions, all_log_probs, _, _ = self.policy.sample_actions(
+        (
+            selected_actions,
+            clipped_actions,
+            all_log_probs,
+            _,
+            _,
+        ) = self.policy.sample_actions(
             vec_obs,
             vis_obs,
             masks=act_masks,
         )
         bc_loss = self._behavioral_cloning_loss(
-            selected_actions, all_log_probs, expert_actions
+            clipped_actions, all_log_probs, expert_actions
         )
         self.optimizer.zero_grad()
         bc_loss.backward()