Fix clear update buffer when trainer stops training, add test

ml-agents/mlagents/trainers/buffer.py

@@ -253,7 +253,7 @@ def truncate(self, max_length: int, sequence_length: int = 1) -> None:
         max_length -= max_length % sequence_length
         if current_length > max_length:
             for _key in self.keys():
-                self[_key] = self[_key][current_length - max_length :]
+                self[_key][:] = self[_key][current_length - max_length :]
 
     def resequence_and_append(
         self,

ml-agents/mlagents/trainers/rl_trainer.py

@@ -73,5 +73,5 @@ def advance(self) -> None:
         Steps the trainer, taking in trajectories and updates if ready
         """
         super().advance()
-        if not self.is_training:
+        if not self.should_still_train:
             self.clear_update_buffer()

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

@@ -152,3 +152,5 @@ def test_buffer_truncate():
     # Test LSTM, truncate should be some multiple of sequence_length
     update_buffer.truncate(4, sequence_length=3)
     assert update_buffer.num_experiences == 3
+    for buffer_field in update_buffer.values():
+        assert isinstance(buffer_field, AgentBuffer.AgentBufferField)

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

@@ -3,13 +3,15 @@
 import mlagents.trainers.tests.mock_brain as mb
 from mlagents.trainers.rl_trainer import RLTrainer
 from mlagents.trainers.tests.test_buffer import construct_fake_buffer
+from mlagents.trainers.agent_processor import AgentManagerQueue
 
 
 def dummy_config():
     return yaml.safe_load(
         """
         summary_path: "test/"
         summary_freq: 1000
+        max_steps: 100
         reward_signals:
           extrinsic:
             strength: 1.0
@@ -75,3 +77,31 @@ def test_clear_update_buffer():
     trainer.clear_update_buffer()
     for _, arr in trainer.update_buffer.items():
         assert len(arr) == 0
+
+
+@mock.patch("mlagents.trainers.rl_trainer.RLTrainer.clear_update_buffer")
+def test_advance(mocked_clear_update_buffer):
+    trainer = create_rl_trainer()
+    trajectory_queue = AgentManagerQueue("testbrain")
+    trainer.subscribe_trajectory_queue(trajectory_queue)
+    time_horizon = 15
+    trajectory = mb.make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        vec_obs_size=1,
+        num_vis_obs=0,
+        action_space=[2],
+    )
+    trajectory_queue.put(trajectory)
+
+    trainer.advance()
+    # 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
+    for _ in range(0, 10):
+        trajectory_queue.put(trajectory)
+        trainer.advance()
+
+    # Check that the buffer has been cleared
+    assert not trainer.should_still_train
+    assert mocked_clear_update_buffer.call_count > 0