#
# 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 PALAlgorithmParameters(DQNAlgorithmParameters):
    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(self.ap.network_wrappers['main'].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