# rl_coach.agents.ddpg_agent — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.agents.ddpg_agent

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    # Copyright (c) 2017 Intel Corporation
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    # 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
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    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
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    import copy
    from typing import Union
    from collections import OrderedDict

    import numpy as np

    from rl_coach.agents.actor_critic_agent import ActorCriticAgent
    from rl_coach.agents.agent import Agent
    from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
    from rl_coach.architectures.head_parameters import DDPGActorHeadParameters, DDPGVHeadParameters
    from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
    from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \
        AgentParameters, EmbedderScheme
    from rl_coach.core_types import ActionInfo, EnvironmentSteps
    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

    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 = [DDPGVHeadParameters()]
            self.optimizer_type = 'Adam'
            self.batch_size = 64
            self.async_training = False
            self.learning_rate = 0.001
            self.adam_optimizer_beta2 = 0.999
            self.optimizer_epsilon = 1e-8
            self.create_target_network = True
            self.shared_optimizer = True
            self.scale_down_gradients_by_number_of_workers_for_sync_training = False
            # self.l2_regularization = 1e-2

    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.optimizer_type = 'Adam'
            self.batch_size = 64
            self.adam_optimizer_beta2 = 0.999
            self.optimizer_epsilon = 1e-8
            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):
        """
        :param num_steps_between_copying_online_weights_to_target: (StepMethod)
            The number of steps between copying the online network weights to the target network weights.

        :param rate_for_copying_weights_to_target: (float)
            When copying the online network weights to the target network weights, a soft update will be used, which
            weight the new online network weights by rate_for_copying_weights_to_target

        :param num_consecutive_playing_steps: (StepMethod)
            The number of consecutive steps to act between every two training iterations

        :param use_target_network_for_evaluation: (bool)
            If set to True, the target network will be used for predicting the actions when choosing actions to act.
            Since the target network weights change more slowly, the predicted actions will be more consistent.

        :param action_penalty: (float)
            The amount by which to penalize the network on high action feature (pre-activation) values.
            This can prevent the actions features from saturating the TanH activation function, and therefore prevent the
            gradients from becoming very low.

        :param clip_critic_targets: (Tuple[float, float] or None)
            The range to clip the critic target to in order to prevent overestimation of the action values.

        :param use_non_zero_discount_for_terminal_states: (bool)
            If set to True, the discount factor will be used for terminal states to bootstrap the next predicted state
            values. If set to False, the terminal states reward will be taken as the target return for the network.
        """
        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=OrderedDict([("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)[0]

            # 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[1]['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