resolve conflicts

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

         seq_len=policy.sequence_length,
     )
     assert log_probs.shape == (64, policy.behavior_spec.action_spec.size)
-    assert entropy.shape == (64, policy.behavior_spec.action_spec.size)
+    assert entropy.shape == (64,)
     for val in values.values():
         assert val.shape == (64,)
 
         )
     else:
         assert log_probs.shape == (64, policy.behavior_spec.action_spec.continuous_size)
-    assert entropies.shape == (64, policy.behavior_spec.action_spec.size)
+    assert entropies.shape == (64,)
 
     if rnn:
         assert memories.shape == (1, 1, policy.m_size)

ml-agents/mlagents/trainers/torch/utils.py

             all_probs = None
         else:
             all_probs = torch.cat(all_probs_list, dim=-1)
-        return log_probs, entropies, all_probs
+        entropy_sum = torch.sum(entropies, dim=1)
+        return log_probs, entropy_sum, all_probs
 
     @staticmethod
     def masked_mean(tensor: torch.Tensor, masks: torch.Tensor) -> torch.Tensor: