Rename append_update_buffer to append_to_update_buffer

ml-agents/mlagents/trainers/buffer.py

         for k in agent_ids:
             self[k].reset_agent()
 
-    def append_update_buffer(
+    def append_to_update_buffer(
         self,
         update_buffer: AgentBuffer,
         agent_id: str,
         :param training_length: The length of the samples that must be appended. If None: only takes one element.
         """
         for agent_id in self.keys():
-            self.append_update_buffer(
+            self.append_to_update_buffer(
                 update_buffer, agent_id, key_list, batch_size, training_length
             )

ml-agents/mlagents/trainers/demo_loader.py

         )
         demo_process_buffer[0]["prev_action"].append(previous_action)
         if next_brain_info.local_done[0]:
-            demo_process_buffer.append_update_buffer(
+            demo_process_buffer.append_to_update_buffer(
-    demo_process_buffer.append_update_buffer(
+    demo_process_buffer.append_to_update_buffer(
         demo_buffer, 0, batch_size=None, training_length=sequence_length
     )
     return demo_buffer

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

                     global_returns
                 )
 
-                self.processing_buffer.append_update_buffer(
+                self.processing_buffer.append_to_update_buffer(
                     self.update_buffer,
                     agent_id,
                     batch_size=None,

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

                         ] = bootstrapping_info.vector_observations[idx]
                     self.processing_buffer[agent_id]["done"][-1] = False
 
-                self.processing_buffer.append_update_buffer(
+                self.processing_buffer.append_to_update_buffer(
                     self.update_buffer,
                     agent_id,
                     batch_size=None,

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

         )
         buffer[0]["memory"].append(np.ones(memory_size))
 
-    buffer.append_update_buffer(
+    buffer.append_to_update_buffer(
         update_buffer, 0, batch_size=None, training_length=sequence_length
     )
     return update_buffer

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

     b[4].reset_agent()
     assert len(b[4]) == 0
     update_buffer = AgentBuffer()
-    b.append_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
-    b.append_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
     assert len(update_buffer["action"]) == 20
     assert np.array(update_buffer["action"]).shape == (20, 2)
 
 def test_buffer_sample():
     b = construct_fake_processing_buffer()
     update_buffer = AgentBuffer()
-    b.append_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
-    b.append_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
     # Test non-LSTM
     mb = update_buffer.sample_mini_batch(batch_size=4, sequence_length=1)
     assert mb.keys() == update_buffer.keys()
 def test_buffer_truncate():
     b = construct_fake_processing_buffer()
     update_buffer = AgentBuffer()
-    b.append_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
-    b.append_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
-    b.append_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
-    b.append_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
+    b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
     # Test LSTM, truncate should be some multiple of sequence_length
     update_buffer.truncate(4, sequence_length=3)
     assert len(update_buffer["action"]) == 3

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

     trainer = create_rl_trainer()
     trainer.processing_buffer = construct_fake_processing_buffer()
     trainer.update_buffer = AgentBuffer()
-    trainer.processing_buffer.append_update_buffer(
+    trainer.processing_buffer.append_to_update_buffer(
         trainer.update_buffer, 2, batch_size=None, training_length=2
     )
     trainer.clear_update_buffer()