Rainbow DQN agent (WIP - still missing dueling and n-step) + adding support for Prioritized ER for C51

rl_coach/agents/categorical_dqn_agent.py

@@ -25,6 +25,7 @@ from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import StateType
 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.prioritized_experience_replay import PrioritizedExperienceReplay
 from rl_coach.schedules import LinearSchedule
 
 
@@ -104,11 +105,22 @@ class CategoricalDQNAgent(ValueOptimizationAgent):
             l = (np.floor(bj)).astype(int)
             m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj))
             m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l))
+
         # total_loss = cross entropy between actual result above and predicted result for the given action
         TD_targets[batches, batch.actions()] = m
 
-        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets)
+        # update errors in prioritized replay buffer
+        importance_weights = batch.info('weight') if isinstance(self.memory, PrioritizedExperienceReplay) else None
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
+                                                               importance_weights=importance_weights)
+
         total_loss, losses, unclipped_grads = result[:3]
 
+        # TODO: fix this spaghetti code
+        if isinstance(self.memory, PrioritizedExperienceReplay):
+            errors = losses[0][np.arange(batch.size), batch.actions()]
+            self.memory.update_priorities(batch.info('idx'), errors)
+
         return total_loss, losses, unclipped_grads
 

rl_coach/agents/rainbow_dqn_agent.py

@@ -0,0 +1,125 @@
+#
+# 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.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.categorical_dqn_agent import CategoricalDQNNetworkParameters, CategoricalDQNAlgorithmParameters, \
+    CategoricalDQNAgent, CategoricalDQNAgentParameters
+from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.tensorflow_components.heads.categorical_q_head import CategoricalQHeadParameters
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.exploration_policies.parameter_noise import ParameterNoiseParameters
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
+from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplayParameters, \
+    PrioritizedExperienceReplay
+from rl_coach.schedules import LinearSchedule
+
+from rl_coach.core_types import StateType
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+
+
+class RainbowDQNNetworkParameters(CategoricalDQNNetworkParameters):
+    def __init__(self):
+        super().__init__()
+
+
+class RainbowDQNAlgorithmParameters(CategoricalDQNAlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+
+
+class RainbowDQNExplorationParameters(ParameterNoiseParameters):
+    def __init__(self, agent_params):
+        super().__init__(agent_params)
+
+
+class RainbowDQNAgentParameters(CategoricalDQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = RainbowDQNAlgorithmParameters()
+        self.exploration = RainbowDQNExplorationParameters(self)
+        self.memory = PrioritizedExperienceReplayParameters()
+        self.network_wrappers = {"main": RainbowDQNNetworkParameters()}
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.rainbow_dqn_agent:RainbowDQNAgent'
+
+
+# Rainbow Deep Q Network - https://arxiv.org/abs/1710.02298
+# Agent implementation is WIP. Currently has:
+# 1. DQN
+# 2. C51
+# 3. Prioritized ER
+# 4. DDQN
+#
+# still missing:
+# 1. N-Step
+# 2. Dueling DQN
+class RainbowDQNAgent(CategoricalDQNAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        ddqn_selected_actions = np.argmax(self.distribution_prediction_to_q_values(
+            self.networks['main'].online_network.predict(batch.next_states(network_keys))), axis=1)
+
+        # for the action we actually took, the error is calculated by the atoms distribution
+        # for all other actions, the error is 0
+        distributed_q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        # only update the action that we have actually done in this transition (using the Double-DQN selected actions)
+        target_actions = ddqn_selected_actions
+        m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size))
+
+        batches = np.arange(self.ap.network_wrappers['main'].batch_size)
+        for j in range(self.z_values.size):
+            tzj = np.fmax(np.fmin(batch.rewards() +
+                                  (1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j],
+                                  self.z_values[self.z_values.size - 1]),
+                          self.z_values[0])
+            bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
+            u = (np.ceil(bj)).astype(int)
+            l = (np.floor(bj)).astype(int)
+            m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj))
+            m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l))
+
+        # total_loss = cross entropy between actual result above and predicted result for the given action
+        TD_targets[batches, batch.actions()] = m
+
+        # update errors in prioritized replay buffer
+        importance_weights = batch.info('weight') if isinstance(self.memory, PrioritizedExperienceReplay) else None
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
+                                                               importance_weights=importance_weights)
+
+        total_loss, losses, unclipped_grads = result[:3]
+
+        # TODO: fix this spaghetti code
+        if isinstance(self.memory, PrioritizedExperienceReplay):
+            errors = losses[0][np.arange(batch.size), batch.actions()]
+            self.memory.update_priorities(batch.info('idx'), errors)
+
+        return total_loss, losses, unclipped_grads
+

rl_coach/architectures/tensorflow_components/heads/rainbow_dqn_head.py

@@ -0,0 +1,44 @@
+#
+# 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.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import tensorflow as tf
+
+from rl_coach.architectures.tensorflow_components.architecture import Dense
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.spaces import SpacesDefinition
+
+from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.core_types import QActionStateValue
+
+
+class RainbowQHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='rainbow_q_head_params', dense_layer=Dense):
+        super().__init__(parameterized_class=RainbowQHead, activation_function=activation_function, name=name,
+                         dense_layer=dense_layer)
+
+
+class RainbowQHead():
+    def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
+                 head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str ='relu',
+                 dense_layer=Dense):
+        super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function,
+                         dense_layer=dense_layer)
+        self.name = 'rainbow_dqn_head'
+        self.num_actions = len(self.spaces.action.actions)
+        self.return_type = QActionStateValue
+
+    def _build_module(self, input_layer):
+        pass

rl_coach/presets/Atari_Rainbow.py

@@ -0,0 +1,46 @@
+from rl_coach.agents.categorical_dqn_agent import CategoricalDQNAgentParameters
+from rl_coach.agents.rainbow_dqn_agent import RainbowDQNAgentParameters
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
+from rl_coach.core_types import EnvironmentSteps, RunPhase
+from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod, SingleLevelSelection
+from rl_coach.environments.gym_environment import Atari, atari_deterministic_v4
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.schedules import LinearSchedule
+
+####################
+# Graph Scheduling #
+####################
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = EnvironmentSteps(50000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentSteps(250000)
+schedule_params.evaluation_steps = EnvironmentSteps(135000)
+schedule_params.heatup_steps = EnvironmentSteps(500)
+
+#########
+# Agent #
+#########
+agent_params = RainbowDQNAgentParameters()
+agent_params.network_wrappers['main'].learning_rate = 0.00025
+agent_params.memory.beta = LinearSchedule(0.4, 1, 12500000)  # 12.5M training iterations = 50M steps = 200M frames
+
+
+###############
+# Environment #
+###############
+env_params = Atari()
+env_params.level = SingleLevelSelection(atari_deterministic_v4)
+
+vis_params = VisualizationParameters()
+vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
+vis_params.dump_mp4 = False
+
+########
+# Test #
+########
+preset_validation_params = PresetValidationParameters()
+preset_validation_params.trace_test_levels = ['breakout', 'pong', 'space_invaders']
+
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+                                    schedule_params=schedule_params, vis_params=vis_params,
+                                    preset_validation_params=preset_validation_params)