add torch no_grad to coma LSTM value computation

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

         all_obs = [current_obs] + team_obs if team_obs is not None else [current_obs]
         all_next_value_mem: Optional[AgentBufferField] = None
         all_next_baseline_mem: Optional[AgentBufferField] = None
-        if self.policy.use_recurrent:
-            (
-                value_estimates,
-                baseline_estimates,
-                all_next_value_mem,
-                all_next_baseline_mem,
-                next_value_mem,
-                next_baseline_mem,
-            ) = self._evaluate_by_sequence_team(
-                current_obs, team_obs, team_actions, _init_value_mem, _init_baseline_mem
-            )
-        else:
-            value_estimates, next_value_mem = self.critic.critic_pass(
-                all_obs, _init_value_mem, sequence_length=batch.num_experiences
-            )
+        with torch.no_grad():
+            if self.policy.use_recurrent:
+                (
+                    value_estimates,
+                    baseline_estimates,
+                    all_next_value_mem,
+                    all_next_baseline_mem,
+                    next_value_mem,
+                    next_baseline_mem,
+                ) = self._evaluate_by_sequence_team(
+                    current_obs,
+                    team_obs,
+                    team_actions,
+                    _init_value_mem,
+                    _init_baseline_mem,
+                )
+            else:
+                value_estimates, next_value_mem = self.critic.critic_pass(
+                    all_obs, _init_value_mem, sequence_length=batch.num_experiences
+                )
-            baseline_estimates, next_baseline_mem = self.critic.baseline(
-                [current_obs],
-                team_obs,
-                team_actions,
-                _init_baseline_mem,
-                sequence_length=batch.num_experiences,
-            )
+                baseline_estimates, next_baseline_mem = self.critic.baseline(
+                    [current_obs],
+                    team_obs,
+                    team_actions,
+                    _init_baseline_mem,
+                    sequence_length=batch.num_experiences,
+                )
         # Store the memory for the next trajectory
         self.value_memory_dict[agent_id] = next_value_mem
         self.baseline_memory_dict[agent_id] = next_baseline_mem