pre-release 0.10.0

rl_coach/agents/ddpg_agent.py

@@ -0,0 +1,192 @@
+#
+# 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 copy
+from typing import Union
+
+import numpy as np
+from rl_coach.agents.actor_critic_agent import ActorCriticAgent
+from rl_coach.agents.agent import Agent
+from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \
+    AgentParameters, InputEmbedderParameters, EmbedderScheme
+from rl_coach.exploration_policies.ou_process import OUProcessParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.spaces import BoxActionSpace, GoalsSpace
+
+from rl_coach.architectures.tensorflow_components.heads.ddpg_actor_head import DDPGActorHeadParameters
+from rl_coach.core_types import ActionInfo, EnvironmentSteps
+
+
+class DDPGCriticNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(batchnorm=True),
+                                            'action': InputEmbedderParameters(scheme=EmbedderScheme.Shallow)}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [VHeadParameters()]
+        self.loss_weights = [1.0]
+        self.rescale_gradient_from_head_by_factor = [1]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 64
+        self.async_training = False
+        self.learning_rate = 0.001
+        self.create_target_network = True
+        self.shared_optimizer = True
+        self.scale_down_gradients_by_number_of_workers_for_sync_training = False
+
+
+class DDPGActorNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(batchnorm=True)}
+        self.middleware_parameters = FCMiddlewareParameters(batchnorm=True)
+        self.heads_parameters = [DDPGActorHeadParameters()]
+        self.loss_weights = [1.0]
+        self.rescale_gradient_from_head_by_factor = [1]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 64
+        self.async_training = False
+        self.learning_rate = 0.0001
+        self.create_target_network = True
+        self.shared_optimizer = True
+        self.scale_down_gradients_by_number_of_workers_for_sync_training = False
+
+
+class DDPGAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
+        self.rate_for_copying_weights_to_target = 0.001
+        self.num_consecutive_playing_steps = EnvironmentSteps(1)
+        self.use_target_network_for_evaluation = False
+        self.action_penalty = 0
+        self.clip_critic_targets = None  # expected to be a tuple of the form (min_clip_value, max_clip_value) or None
+        self.use_non_zero_discount_for_terminal_states = False
+
+
+class DDPGAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=DDPGAlgorithmParameters(),
+                         exploration=OUProcessParameters(),
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks={"actor": DDPGActorNetworkParameters(),
+                                    "critic": DDPGCriticNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.ddpg_agent:DDPGAgent'
+
+
+# Deep Deterministic Policy Gradients Network - https://arxiv.org/pdf/1509.02971.pdf
+class DDPGAgent(ActorCriticAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+        self.q_values = self.register_signal("Q")
+        self.TD_targets_signal = self.register_signal("TD targets")
+        self.action_signal = self.register_signal("actions")
+
+    def learn_from_batch(self, batch):
+        actor = self.networks['actor']
+        critic = self.networks['critic']
+
+        actor_keys = self.ap.network_wrappers['actor'].input_embedders_parameters.keys()
+        critic_keys = self.ap.network_wrappers['critic'].input_embedders_parameters.keys()
+
+        # TD error = r + discount*max(q_st_plus_1) - q_st
+        next_actions, actions_mean = actor.parallel_prediction([
+            (actor.target_network, batch.next_states(actor_keys)),
+            (actor.online_network, batch.states(actor_keys))
+        ])
+
+        critic_inputs = copy.copy(batch.next_states(critic_keys))
+        critic_inputs['action'] = next_actions
+        q_st_plus_1 = critic.target_network.predict(critic_inputs)
+
+        # calculate the bootstrapped TD targets while discounting terminal states according to
+        # use_non_zero_discount_for_terminal_states
+        if self.ap.algorithm.use_non_zero_discount_for_terminal_states:
+            TD_targets = batch.rewards(expand_dims=True) + self.ap.algorithm.discount * q_st_plus_1
+        else:
+            TD_targets = batch.rewards(expand_dims=True) + \
+                         (1.0 - batch.game_overs(expand_dims=True)) * self.ap.algorithm.discount * q_st_plus_1
+
+        # clip the TD targets to prevent overestimation errors
+        if self.ap.algorithm.clip_critic_targets:
+            TD_targets = np.clip(TD_targets, *self.ap.algorithm.clip_critic_targets)
+
+        self.TD_targets_signal.add_sample(TD_targets)
+
+        # get the gradients of the critic output with respect to the action
+        critic_inputs = copy.copy(batch.states(critic_keys))
+        critic_inputs['action'] = actions_mean
+        action_gradients = critic.online_network.predict(critic_inputs,
+                                                         outputs=critic.online_network.gradients_wrt_inputs[0]['action'])
+
+        # train the critic
+        critic_inputs = copy.copy(batch.states(critic_keys))
+        critic_inputs['action'] = batch.actions(len(batch.actions().shape) == 1)
+        result = critic.train_and_sync_networks(critic_inputs, TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        # apply the gradients from the critic to the actor
+        initial_feed_dict = {actor.online_network.gradients_weights_ph[0]: -action_gradients}
+        gradients = actor.online_network.predict(batch.states(actor_keys),
+                                                 outputs=actor.online_network.weighted_gradients[0],
+                                                 initial_feed_dict=initial_feed_dict)
+
+        if actor.has_global:
+            actor.apply_gradients_to_global_network(gradients)
+            actor.update_online_network()
+        else:
+            actor.apply_gradients_to_online_network(gradients)
+
+        return total_loss, losses, unclipped_grads
+
+    def train(self):
+        return Agent.train(self)
+
+    def choose_action(self, curr_state):
+        if not (isinstance(self.spaces.action, BoxActionSpace) or isinstance(self.spaces.action, GoalsSpace)):
+            raise ValueError("DDPG works only for continuous control problems")
+        # convert to batch so we can run it through the network
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'actor')
+        if self.ap.algorithm.use_target_network_for_evaluation:
+            actor_network = self.networks['actor'].target_network
+        else:
+            actor_network = self.networks['actor'].online_network
+
+        action_values = actor_network.predict(tf_input_state).squeeze()
+
+        action = self.exploration_policy.get_action(action_values)
+
+        self.action_signal.add_sample(action)
+
+        # get q value
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'critic')
+        action_batch = np.expand_dims(action, 0)
+        if type(action) != np.ndarray:
+            action_batch = np.array([[action]])
+        tf_input_state['action'] = action_batch
+        q_value = self.networks['critic'].online_network.predict(tf_input_state)[0]
+        self.q_values.add_sample(q_value)
+
+        action_info = ActionInfo(action=action,
+                                 action_value=q_value)
+
+        return action_info