waiting for a new checkpoint if it's available

rl_coach/agents/agent.py

@@ -81,7 +81,7 @@ class Agent(AgentInterface):
                 self.memory_backend = get_memory_backend(self.ap.memory.memory_backend_params)
 
                 if self.ap.memory.memory_backend_params.run_type == 'trainer':
-                    self.memory_backend.subscribe(self.memory)
+                    self.memory_backend.subscribe(self)
                 else:
                     self.memory.set_memory_backend(self.memory_backend)
 
@@ -523,8 +523,7 @@ class Agent(AgentInterface):
         Determine if online weights should be copied to the target.
         :return: boolean: True if the online weights should be copied to the target.
         """
-        if hasattr(self.ap.memory, 'memory_backend_params'):
-            self.total_steps_counter = self.call_memory('num_transitions')
+
         # update the target network of every network that has a target network
         step_method = self.ap.algorithm.num_steps_between_copying_online_weights_to_target
         if step_method.__class__ == TrainingSteps:
@@ -546,7 +545,7 @@ class Agent(AgentInterface):
         :return:  boolean: True if we should start a training phase
         """
 
-        should_update = self._should_train_helper(wait_for_full_episode)
+        should_update = self._should_train_helper(wait_for_full_episode=wait_for_full_episode)
 
         step_method = self.ap.algorithm.num_consecutive_playing_steps
 
@@ -560,10 +559,8 @@ class Agent(AgentInterface):
 
     def _should_train_helper(self, wait_for_full_episode=False):
 
-        if hasattr(self.ap.memory, 'memory_backend_params'):
-            self.total_steps_counter = self.call_memory('num_transitions')
-
         step_method = self.ap.algorithm.num_consecutive_playing_steps
+
         if step_method.__class__ == EnvironmentEpisodes:
             should_update = (self.current_episode - self.last_training_phase_step) >= step_method.num_steps
 

rl_coach/agents/clipped_ppo_agent.py

@@ -251,6 +251,9 @@ class ClippedPPOAgent(ActorCriticAgent):
         # clean memory
         self.call_memory('clean')
 
+    def _should_train_helper(self, wait_for_full_episode=True):
+        return super()._should_train_helper(True)
+
     def train(self):
         if self._should_train(wait_for_full_episode=True):
             for network in self.networks.values():

rl_coach/agents/ppo_agent.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2017 Intel Corporation 
+# Copyright (c) 2017 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -310,6 +310,9 @@ class PPOAgent(ActorCriticAgent):
         # clean memory
         self.call_memory('clean')
 
+    def _should_train_helper(self):
+        return super()._should_train_helper(True)
+
     def train(self):
         loss = 0
         if self._should_train(wait_for_full_episode=True):