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

# Source code for rl_coach.agents.pal_agent

    #
    # 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.
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    from typing import Union

    import numpy as np

    from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters
    from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
    from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters

    class PALAlgorithmParameters(DQNAlgorithmParameters):
        """
        :param pal_alpha: (float)
            A factor that weights the amount by which the advantage learning update will be taken into account.

        :param persistent_advantage_learning: (bool)
            If set to True, the persistent mode of advantage learning will be used, which encourages the agent to take
            the same actions one after the other instead of changing actions.

        :param monte_carlo_mixing_rate: (float)
            The amount of monte carlo values to mix into the targets of the network. The monte carlo values are just the
            total discounted returns, and they can help reduce the time it takes for the network to update to the newly
            seen values, since it is not based on bootstrapping the current network values.
        """
        def __init__(self):
            super().__init__()
            self.pal_alpha = 0.9
            self.persistent_advantage_learning = False
            self.monte_carlo_mixing_rate = 0.1

    class PALAgentParameters(DQNAgentParameters):
        def __init__(self):
            super().__init__()
            self.algorithm = PALAlgorithmParameters()
            self.memory = EpisodicExperienceReplayParameters()

        @property
        def path(self):
            return 'rl_coach.agents.pal_agent:PALAgent'

    # Persistent Advantage Learning - https://arxiv.org/pdf/1512.04860.pdf
    class PALAgent(ValueOptimizationAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)
            self.alpha = agent_parameters.algorithm.pal_alpha
            self.persistent = agent_parameters.algorithm.persistent_advantage_learning
            self.monte_carlo_mixing_rate = agent_parameters.algorithm.monte_carlo_mixing_rate

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

            # next state values
            q_st_plus_1_target, q_st_plus_1_online = self.networks['main'].parallel_prediction([
                (self.networks['main'].target_network, batch.next_states(network_keys)),
                (self.networks['main'].online_network, batch.next_states(network_keys))
            ])
            selected_actions = np.argmax(q_st_plus_1_online, 1)
            v_st_plus_1_target = np.max(q_st_plus_1_target, 1)

            # current state values
            q_st_target, q_st_online = self.networks['main'].parallel_prediction([
                (self.networks['main'].target_network, batch.states(network_keys)),
                (self.networks['main'].online_network, batch.states(network_keys))
            ])
            v_st_target = np.max(q_st_target, 1)

            # calculate TD error
            TD_targets = np.copy(q_st_online)
            total_returns = batch.n_step_discounted_rewards()
            for i in range(batch.size):
                TD_targets[i, batch.actions()[i]] = batch.rewards()[i] + \
                                            (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
                                                         q_st_plus_1_target[i][selected_actions[i]]
                advantage_learning_update = v_st_target[i] - q_st_target[i, batch.actions()[i]]
                next_advantage_learning_update = v_st_plus_1_target[i] - q_st_plus_1_target[i, selected_actions[i]]
                # Persistent Advantage Learning or Regular Advantage Learning
                if self.persistent:
                    TD_targets[i, batch.actions()[i]] -= self.alpha * min(advantage_learning_update, next_advantage_learning_update)
                else:
                    TD_targets[i, batch.actions()[i]] -= self.alpha * advantage_learning_update

                # mixing monte carlo updates
                monte_carlo_target = total_returns[i]
                TD_targets[i, batch.actions()[i]] = (1 - self.monte_carlo_mixing_rate) * TD_targets[i, batch.actions()[i]] \
                                            + self.monte_carlo_mixing_rate * monte_carlo_target

            result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets)
            total_loss, losses, unclipped_grads = result[:3]

            return total_loss, losses, unclipped_grads