remove warning prints

ml-agents/mlagents/trainers/agent_processor.py

             self._process_step(
                 ongoing_step, global_id, decision_steps.agent_id_to_index[local_id]
             )
-            if decision_steps.agent_id_to_index[local_id] == 9:
-                print("wrong index", ongoing_step, global_id, decision_steps.agent_id, decision_steps.obs, decision_steps.reward, terminal_steps.obs, terminal_steps.agent_id)
+            #if decision_steps.agent_id_to_index[local_id] == 9:
+            #    print("wrong index", ongoing_step, global_id, decision_steps.agent_id, decision_steps.obs, decision_steps.reward, terminal_steps.obs, terminal_steps.agent_id)
 
         for _gid in action_global_agent_ids:
             # If the ID doesn't have a last step result, the agent just reset,
     ) -> None:
         terminated = isinstance(step, TerminalStep)
         stored_decision_step, idx = self.last_step_result.get(global_id, (None, None))
-        if idx == 9:
-            print("Index was 9", global_id, stored_decision_step.agent_id)
+        #if idx == 9:
+        #    print("Index was 9", global_id, stored_decision_step.agent_id)
         stored_take_action_outputs = self.last_take_action_outputs.get(global_id, None)
         if not terminated:
             # Index is needed to grab from last_take_action_outputs

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

 
         if total_enc_size == 0:
             raise Exception("No valid inputs to network.")
-        for _, tens in list(self.transformer.named_parameters()):
-            tens.retain_grad()
-        for _, tens in list(self.entity_embedding.named_parameters()):
-            tens.retain_grad()
+        #for _, tens in list(self.transformer.named_parameters()):
+        #    tens.retain_grad()
+        #for _, tens in list(self.entity_embedding.named_parameters()):
+        #    tens.retain_grad()
-        for _, tens in list(self.linear_encoder.named_parameters()):
-            tens.retain_grad()
-        for processor in self.processors:
-            if processor is not None:
-                for _, tens in list(processor.named_parameters()):
-                    tens.retain_grad()
+        #for _, tens in list(self.linear_encoder.named_parameters()):
+        #    tens.retain_grad()
+        #for processor in self.processors:
+        #    if processor is not None:
+        #        for _, tens in list(processor.named_parameters()):
+        #            tens.retain_grad()
 
         if self.use_lstm:
             self.lstm = LSTM(self.h_size, self.m_size)