Fix stall in ghost trainer non-threaded

ml-agents/mlagents/trainers/agent_processor.py

 
         pass
 
-    def __init__(self, behavior_id: str, maxlen: int = 20):
+    def __init__(self, behavior_id: str, maxlen: int = 0):
         """
         Initializes an AgentManagerQueue. Note that we can give it a behavior_id so that it can be identified
         separately from an AgentManager.
         behavior_id: str,
         stats_reporter: StatsReporter,
         max_trajectory_length: int = sys.maxsize,
+        threaded: bool = True,
+        trajectory_queue_len = 20 if threaded else 0
-            self.behavior_id
+            self.behavior_id, maxlen=trajectory_queue_len
         )
         # NOTE: we make policy queues of infinite length to avoid lockups of the trainers.
         # In the environment manager, we make sure to empty the policy queue before continuing to produce steps.

ml-agents/mlagents/trainers/trainer_controller.py

             name_behavior_id,
             trainer.stats_reporter,
             trainer.parameters.get("time_horizon", sys.maxsize),
+            threaded=trainer.threaded,
         )
         env_manager.set_agent_manager(name_behavior_id, agent_manager)
         env_manager.set_policy(name_behavior_id, policy)