# Source code for rl_coach.agents.mmc_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.
    #

    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 MixedMonteCarloAlgorithmParameters(DQNAlgorithmParameters):
        """
        :param monte_carlo_mixing_rate: (float)
            The mixing rate is used for setting the amount of monte carlo estimate (full return) that will be mixes into
            the single-step bootstrapped targets.
        """
        def __init__(self):
            super().__init__()
            self.monte_carlo_mixing_rate = 0.1

    class MixedMonteCarloAgentParameters(DQNAgentParameters):
        def __init__(self):
            super().__init__()
            self.algorithm = MixedMonteCarloAlgorithmParameters()
            self.memory = EpisodicExperienceReplayParameters()

        @property
        def path(self):
            return 'rl_coach.agents.mmc_agent:MixedMonteCarloAgent'

    class MixedMonteCarloAgent(ValueOptimizationAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)
            self.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()

            # for the 1-step, we use the double-dqn target. hence actions are taken greedily according to the online network
            selected_actions = np.argmax(self.networks['main'].online_network.predict(batch.next_states(network_keys)), 1)

            # TD_targets are initialized with the current prediction so that we will
            #  only update the action that we have actually done in this transition
            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))
            ])

            total_returns = batch.n_step_discounted_rewards()

            for i in range(self.ap.network_wrappers['main'].batch_size):
                one_step_target = batch.rewards()[i] + \
                                  (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
                                  q_st_plus_1[i][selected_actions[i]]
                monte_carlo_target = total_returns[i]
                TD_targets[i, batch.actions()[i]] = (1 - self.mixing_rate) * one_step_target + \
                                                    self.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