Revert to get_nowait method in AgentManagerQueue

ml-agents/mlagents/trainers/agent_processor.py

     def empty(self) -> bool:
         return self._queue.empty()
 
-    def get(self, block: bool = True, timeout: float = None) -> T:
+    def get_nowait(self, block: bool = True, timeout: float = None) -> T:
-        Gets the next item from the queue.
-        :param block: Block if the queue is empty. If False, exit immediately and
-            throw an AgentManagerQueue.Empty exception. (default = True)
-        :param timeout: Timeout for blocking get. If a positive float, wait timeout seconds
-            before throwing an AgentManagerQueue.Empty exception. (default = None)
+        Gets the next item from the queue, throwing an AgentManagerQueue.Empty exception
+        if the queue is empty.
-            return self._queue.get(block=block, timeout=timeout)
+            return self._queue.get_nowait()
         except queue.Empty:
             raise self.Empty("The AgentManagerQueue is empty.")
 

ml-agents/mlagents/trainers/ghost/trainer.py

                     # This ensures that even if the queue is being filled faster than it is
                     # being emptied, the trajectories in the queue are on-policy.
                     for _ in range(trajectory_queue.qsize()):
-                        t = trajectory_queue.get(block=False)
+                        t = trajectory_queue.get_nowait()
                         # adds to wrapped trainers queue
                         internal_trajectory_queue.put(t)
                         self._process_trajectory(t)
                 # Dump trajectories from non-learning policy
                 try:
                     for _ in range(trajectory_queue.qsize()):
-                        t = trajectory_queue.get(block=False)
+                        t = trajectory_queue.get_nowait()
                         # count ghost steps
                         self.ghost_step += len(t.steps)
                 except AgentManagerQueue.Empty:
         for brain_name in self._internal_policy_queues:
             internal_policy_queue = self._internal_policy_queues[brain_name]
             try:
-                policy = cast(TFPolicy, internal_policy_queue.get(block=False))
+                policy = cast(TFPolicy, internal_policy_queue.get_nowait())
                 self.current_policy_snapshot[brain_name] = policy.get_weights()
             except AgentManagerQueue.Empty:
                 pass

ml-agents/mlagents/trainers/tests/test_agent_processor.py

     assert queue.empty()
     queue.put(trajectory)
     assert not queue.empty()
-    queue_traj = queue.get(block=False)
+    queue_traj = queue.get_nowait()
     assert isinstance(queue_traj, Trajectory)
     assert queue.empty()
 

ml-agents/mlagents/trainers/tests/test_ghost.py

     trainer._swap_snapshots()
     assert policy_queue0.empty() and not policy_queue1.empty()
     # clear
-    policy_queue1.get(block=False)
+    policy_queue1.get_nowait()
 
     mock_brain = mb.setup_mock_brain(
         False,

ml-agents/mlagents/trainers/tests/test_rl_trainer.py

     trajectory_queue.put(trajectory)
 
     trainer.advance()
-    policy_queue.get(block=False)
+    policy_queue.get_nowait()
     # Check that get_step is correct
     assert trainer.get_step == time_horizon
     # Check that we can turn off the trainer and that the buffer is cleared
         # Check that there is stuff in the policy queue
-        policy_queue.get(block=False)
+        policy_queue.get_nowait()
 
     # Check that if the policy doesn't update, we don't push it to the queue
     trainer.set_is_policy_updating(False)
         # Check that there nothing  in the policy queue
         with pytest.raises(AgentManagerQueue.Empty):
-            policy_queue.get(block=False)
+            policy_queue.get_nowait()
 
     # Check that the buffer has been cleared
     assert not trainer.should_still_train

ml-agents/mlagents/trainers/tests/test_sac.py

     )
 
     # Make sure there is a policy on the queue
-    policy_queue.get(block=False)
+    policy_queue.get_nowait()
 
     # Add another trajectory. Since this is less than 20 steps total (enough for)
     # two updates, there should NOT be a policy on the queue.
     trajectory_queue.put(trajectory)
     trainer.advance()
     with pytest.raises(AgentManagerQueue.Empty):
-        policy_queue.get(block=False)
+        policy_queue.get_nowait()
 
 
 def test_bad_config(dummy_config):

ml-agents/mlagents/trainers/trainer/rl_trainer.py

                 # being emptied, the trajectories in the queue are on-policy.
                 for _ in range(traj_queue.qsize()):
                     try:
-                        t = traj_queue.get(block=False)
+                        t = traj_queue.get_nowait()
                         self._process_trajectory(t)
                     except AgentManagerQueue.Empty:
                         break