Adding initial interface for backend and redis pubsub (#19)

* Adding initial interface for backend and redis pubsub

* Addressing comments, adding super in all memories

* Removing distributed experience replay

rl_coach/agents/agent.py

@@ -33,6 +33,7 @@ from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperi
 from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace
 from rl_coach.utils import Signal, force_list
 from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
+from rl_coach.memories.backend.memory_impl import get_memory_backend
 
 
 class Agent(AgentInterface):
@@ -76,6 +77,14 @@ class Agent(AgentInterface):
             # modules
             self.memory = dynamic_import_and_instantiate_module_from_params(self.ap.memory)
 
+            if hasattr(self.ap.memory, 'memory_backend_params'):
+                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)
+                else:
+                    self.memory.set_memory_backend(self.memory_backend)
+
             if agent_parameters.memory.load_memory_from_file_path:
                 screen.log_title("Loading replay buffer from pickle. Pickle path: {}"
                                  .format(agent_parameters.memory.load_memory_from_file_path))
@@ -534,6 +543,9 @@ class Agent(AgentInterface):
         Determine if we should start a training phase according to the number of steps passed since the last training
         :return:  boolean: True if we should start a training phase
         """
+
+        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

@@ -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.

rl_coach/agents/dqn_agent.py

@@ -27,7 +27,6 @@ from rl_coach.architectures.tensorflow_components.embedders.embedder import Inpu
 from rl_coach.core_types import EnvironmentSteps
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters
 from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
-from rl_coach.memories.non_episodic.distributed_experience_replay import DistributedExperienceReplayParameters
 from rl_coach.schedules import LinearSchedule
 
 
@@ -51,20 +50,6 @@ class DQNNetworkParameters(NetworkParameters):
         self.create_target_network = True
 
 
-class DQNAgentParametersDistributed(AgentParameters):
-    def __init__(self):
-        super().__init__(algorithm=DQNAlgorithmParameters(),
-                         exploration=EGreedyParameters(),
-                         memory=DistributedExperienceReplayParameters(),
-                         networks={"main": DQNNetworkParameters()})
-        self.exploration.epsilon_schedule = LinearSchedule(1, 0.1, 1000000)
-        self.exploration.evaluation_epsilon = 0.05
-
-    @property
-    def path(self):
-        return 'rl_coach.agents.dqn_agent:DQNAgent'
-
-
 class DQNAgentParameters(AgentParameters):
     def __init__(self):
         super().__init__(algorithm=DQNAlgorithmParameters(),