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

# Source code for rl_coach.agents.clipped_ppo_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
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    #      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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    # See the License for the specific language governing permissions and
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    import copy
    from collections import OrderedDict
    from random import shuffle
    from typing import Union

    import numpy as np

    from rl_coach.agents.actor_critic_agent import ActorCriticAgent
    from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
    from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
    from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
    from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
    from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
        AgentParameters
    from rl_coach.core_types import EnvironmentSteps, Batch, EnvResponse, StateType
    from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
    from rl_coach.exploration_policies.categorical import CategoricalParameters
    from rl_coach.logger import screen
    from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
    from rl_coach.schedules import ConstantSchedule
    from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace

    class ClippedPPONetworkParameters(NetworkParameters):
        def __init__(self):
            super().__init__()
            self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='tanh')}
            self.middleware_parameters = FCMiddlewareParameters(activation_function='tanh')
            self.heads_parameters = [VHeadParameters(), PPOHeadParameters()]
            self.batch_size = 64
            self.optimizer_type = 'Adam'
            self.clip_gradients = None
            self.use_separate_networks_per_head = True
            self.async_training = False
            self.l2_regularization = 0

            # The target network is used in order to freeze the old policy, while making updates to the new one
            # in train_network()
            self.create_target_network = True
            self.shared_optimizer = True
            self.scale_down_gradients_by_number_of_workers_for_sync_training = True

    class ClippedPPOAlgorithmParameters(AlgorithmParameters):
        """
        :param policy_gradient_rescaler: (PolicyGradientRescaler)
            This represents how the critic will be used to update the actor. The critic value function is typically used
            to rescale the gradients calculated by the actor. There are several ways for doing this, such as using the
            advantage of the action, or the generalized advantage estimation (GAE) value.

        :param gae_lambda: (float)
            The :math:`\lambda` value is used within the GAE function in order to weight different bootstrap length
            estimations. Typical values are in the range 0.9-1, and define an exponential decay over the different
            n-step estimations.

        :param clip_likelihood_ratio_using_epsilon: (float)
            If not None, the likelihood ratio between the current and new policy in the PPO loss function will be
            clipped to the range [1-clip_likelihood_ratio_using_epsilon, 1+clip_likelihood_ratio_using_epsilon].
            This is typically used in the Clipped PPO version of PPO, and should be set to None in regular PPO
            implementations.

        :param value_targets_mix_fraction: (float)
            The targets for the value network are an exponential weighted moving average which uses this mix fraction to
            define how much of the new targets will be taken into account when calculating the loss.
            This value should be set to the range (0,1], where 1 means that only the new targets will be taken into account.

        :param estimate_state_value_using_gae: (bool)
            If set to True, the state value will be estimated using the GAE technique.

        :param use_kl_regularization: (bool)
            If set to True, the loss function will be regularized using the KL diveregence between the current and new
            policy, to bound the change of the policy during the network update.

        :param beta_entropy: (float)
            An entropy regulaization term can be added to the loss function in order to control exploration. This term
            is weighted using the :math:`\beta` value defined by beta_entropy.

        :param optimization_epochs: (int)
            For each training phase, the collected dataset will be used for multiple epochs, which are defined by the
            optimization_epochs value.

        :param optimization_epochs: (Schedule)
            Can be used to define a schedule over the clipping of the likelihood ratio.

        """
        def __init__(self):
            super().__init__()
            self.num_episodes_in_experience_replay = 1000000
            self.policy_gradient_rescaler = PolicyGradientRescaler.GAE
            self.gae_lambda = 0.95
            self.use_kl_regularization = False
            self.clip_likelihood_ratio_using_epsilon = 0.2
            self.estimate_state_value_using_gae = True
            self.beta_entropy = 0.01  # should be 0 for mujoco
            self.num_consecutive_playing_steps = EnvironmentSteps(2048)
            self.optimization_epochs = 10
            self.normalization_stats = None
            self.clipping_decay_schedule = ConstantSchedule(1)
            self.act_for_full_episodes = True

    class ClippedPPOAgentParameters(AgentParameters):
        def __init__(self):
            super().__init__(algorithm=ClippedPPOAlgorithmParameters(),
                             exploration={DiscreteActionSpace: CategoricalParameters(),
                                          BoxActionSpace: AdditiveNoiseParameters()},
                             memory=EpisodicExperienceReplayParameters(),
                             networks={"main": ClippedPPONetworkParameters()})

        @property
        def path(self):
            return 'rl_coach.agents.clipped_ppo_agent:ClippedPPOAgent'

    # Clipped Proximal Policy Optimization - https://arxiv.org/abs/1707.06347
    class ClippedPPOAgent(ActorCriticAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)
            # signals definition
            self.value_loss = self.register_signal('Value Loss')
            self.policy_loss = self.register_signal('Policy Loss')
            self.total_kl_divergence_during_training_process = 0.0
            self.unclipped_grads = self.register_signal('Grads (unclipped)')
            self.value_targets = self.register_signal('Value Targets')
            self.kl_divergence = self.register_signal('KL Divergence')
            self.likelihood_ratio = self.register_signal('Likelihood Ratio')
            self.clipped_likelihood_ratio = self.register_signal('Clipped Likelihood Ratio')

        def set_session(self, sess):
            super().set_session(sess)
            if self.ap.algorithm.normalization_stats is not None:
                self.ap.algorithm.normalization_stats.set_session(sess)

        def fill_advantages(self, batch):
            network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()

            current_state_values = self.networks['main'].online_network.predict(batch.states(network_keys))[0]
            current_state_values = current_state_values.squeeze()
            self.state_values.add_sample(current_state_values)

            # calculate advantages
            advantages = []
            value_targets = []
            total_returns = batch.n_step_discounted_rewards()

            if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE:
                advantages = total_returns - current_state_values
            elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE:
                # get bootstraps
                episode_start_idx = 0
                advantages = np.array([])
                value_targets = np.array([])
                for idx, game_over in enumerate(batch.game_overs()):
                    if game_over:
                        # get advantages for the rollout
                        value_bootstrapping = np.zeros((1,))
                        rollout_state_values = np.append(current_state_values[episode_start_idx:idx+1], value_bootstrapping)

                        rollout_advantages, gae_based_value_targets = \
                            self.get_general_advantage_estimation_values(batch.rewards()[episode_start_idx:idx+1],
                                                                         rollout_state_values)
                        episode_start_idx = idx + 1
                        advantages = np.append(advantages, rollout_advantages)
                        value_targets = np.append(value_targets, gae_based_value_targets)
            else:
                screen.warning("WARNING: The requested policy gradient rescaler is not available")

            # standardize
            advantages = (advantages - np.mean(advantages)) / np.std(advantages)

            for transition, advantage, value_target in zip(batch.transitions, advantages, value_targets):
                transition.info['advantage'] = advantage
                transition.info['gae_based_value_target'] = value_target

            self.action_advantages.add_sample(advantages)

        def train_network(self, batch, epochs):
            batch_results = []
            for j in range(epochs):
                batch.shuffle()
                batch_results = {
                    'total_loss': [],
                    'losses': [],
                    'unclipped_grads': [],
                    'kl_divergence': [],
                    'entropy': []
                }

                fetches = [self.networks['main'].online_network.output_heads[1].kl_divergence,
                           self.networks['main'].online_network.output_heads[1].entropy,
                           self.networks['main'].online_network.output_heads[1].likelihood_ratio,
                           self.networks['main'].online_network.output_heads[1].clipped_likelihood_ratio]

                # TODO-fixme if batch.size / self.ap.network_wrappers['main'].batch_size is not an integer, we do not train on
                #  some of the data
                for i in range(int(batch.size / self.ap.network_wrappers['main'].batch_size)):
                    start = i * self.ap.network_wrappers['main'].batch_size
                    end = (i + 1) * self.ap.network_wrappers['main'].batch_size

                    network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
                    actions = batch.actions()[start:end]
                    gae_based_value_targets = batch.info('gae_based_value_target')[start:end]
                    if not isinstance(self.spaces.action, DiscreteActionSpace) and len(actions.shape) == 1:
                        actions = np.expand_dims(actions, -1)

                    # get old policy probabilities and distribution

                    # TODO-perf - the target network ("old_policy") is not changing. this can be calculated once for all epochs.
                    # the shuffling being done, should only be performed on the indices.
                    result = self.networks['main'].target_network.predict({k: v[start:end] for k, v in batch.states(network_keys).items()})
                    old_policy_distribution = result[1:]

                    total_returns = batch.n_step_discounted_rewards(expand_dims=True)

                    # calculate gradients and apply on both the local policy network and on the global policy network
                    if self.ap.algorithm.estimate_state_value_using_gae:
                        value_targets = np.expand_dims(gae_based_value_targets, -1)
                    else:
                        value_targets = total_returns[start:end]

                    inputs = copy.copy({k: v[start:end] for k, v in batch.states(network_keys).items()})
                    inputs['output_1_0'] = actions

                    # The old_policy_distribution needs to be represented as a list, because in the event of
                    # discrete controls, it has just a mean. otherwise, it has both a mean and standard deviation
                    for input_index, input in enumerate(old_policy_distribution):
                        inputs['output_1_{}'.format(input_index + 1)] = input

                    # update the clipping decay schedule value
                    inputs['output_1_{}'.format(len(old_policy_distribution)+1)] = \
                        self.ap.algorithm.clipping_decay_schedule.current_value

                    total_loss, losses, unclipped_grads, fetch_result = \
                        self.networks['main'].train_and_sync_networks(
                            inputs, [value_targets, batch.info('advantage')[start:end]], additional_fetches=fetches
                        )

                    batch_results['total_loss'].append(total_loss)
                    batch_results['losses'].append(losses)
                    batch_results['unclipped_grads'].append(unclipped_grads)
                    batch_results['kl_divergence'].append(fetch_result[0])
                    batch_results['entropy'].append(fetch_result[1])

                    self.unclipped_grads.add_sample(unclipped_grads)
                    self.value_targets.add_sample(value_targets)
                    self.likelihood_ratio.add_sample(fetch_result[2])
                    self.clipped_likelihood_ratio.add_sample(fetch_result[3])

                for key in batch_results.keys():
                    batch_results[key] = np.mean(batch_results[key], 0)

                self.value_loss.add_sample(batch_results['losses'][0])
                self.policy_loss.add_sample(batch_results['losses'][1])
                self.loss.add_sample(batch_results['total_loss'])

                if self.ap.network_wrappers['main'].learning_rate_decay_rate != 0:
                    curr_learning_rate = self.networks['main'].online_network.get_variable_value(
                        self.networks['main'].online_network.adaptive_learning_rate_scheme)
                    self.curr_learning_rate.add_sample(curr_learning_rate)
                else:
                    curr_learning_rate = self.ap.network_wrappers['main'].learning_rate

                # log training parameters
                screen.log_dict(
                    OrderedDict([
                        ("Surrogate loss", batch_results['losses'][1]),
                        ("KL divergence", batch_results['kl_divergence']),
                        ("Entropy", batch_results['entropy']),
                        ("training epoch", j),
                        ("learning_rate", curr_learning_rate)
                    ]),
                    prefix="Policy training"
                )

            self.total_kl_divergence_during_training_process = batch_results['kl_divergence']
            self.entropy.add_sample(batch_results['entropy'])
            self.kl_divergence.add_sample(batch_results['kl_divergence'])
            return batch_results['losses']

        def post_training_commands(self):
            # clean memory
            self.call_memory('clean')

        def train(self):
            if self._should_train():
                for network in self.networks.values():
                    network.set_is_training(True)

                dataset = self.memory.transitions
                dataset = self.pre_network_filter.filter(dataset, deep_copy=False)
                batch = Batch(dataset)

                for training_step in range(self.ap.algorithm.num_consecutive_training_steps):
                    self.networks['main'].sync()
                    self.fill_advantages(batch)

                    # take only the requested number of steps
                    if isinstance(self.ap.algorithm.num_consecutive_playing_steps, EnvironmentSteps):
                        dataset = dataset[:self.ap.algorithm.num_consecutive_playing_steps.num_steps]
                    shuffle(dataset)
                    batch = Batch(dataset)

                    self.train_network(batch, self.ap.algorithm.optimization_epochs)

                for network in self.networks.values():
                    network.set_is_training(False)

                self.post_training_commands()
                self.training_iteration += 1
                # should be done in order to update the data that has been accumulated * while not playing *
                self.update_log()
                return None

        def run_pre_network_filter_for_inference(self, state: StateType, update_internal_state: bool=False):
            dummy_env_response = EnvResponse(next_state=state, reward=0, game_over=False)
            return self.pre_network_filter.filter(dummy_env_response, update_internal_state=False)[0].next_state

        def choose_action(self, curr_state):
            self.ap.algorithm.clipping_decay_schedule.step()
            return super().choose_action(curr_state)