#
# 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 List

import numpy as np

from rl_coach.core_types import RunPhase, ActionType
from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy
from rl_coach.schedules import Schedule, LinearSchedule
from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace
from rl_coach.utils import dynamic_import_and_instantiate_module_from_params


class EGreedyParameters(ExplorationParameters):
    def __init__(self):
        super().__init__()
        self.epsilon_schedule = LinearSchedule(0.5, 0.01, 50000)
        self.evaluation_epsilon = 0.05
        self.continuous_exploration_policy_parameters = AdditiveNoiseParameters()
        self.continuous_exploration_policy_parameters.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
        # for continuous control -
        # (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)

    @property
    def path(self):
        return 'rl_coach.exploration_policies.e_greedy:EGreedy'


class EGreedy(ExplorationPolicy):
    def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule,
                 evaluation_epsilon: float,
                 continuous_exploration_policy_parameters: ExplorationParameters=AdditiveNoiseParameters()):
        """
        :param action_space: the action space used by the environment
        :param epsilon_schedule: a schedule for the epsilon values
        :param evaluation_epsilon: the epsilon value to use for evaluation phases
        :param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
                                                         if the e-greedy is used for a continuous policy
        """
        super().__init__(action_space)
        self.epsilon_schedule = epsilon_schedule
        self.evaluation_epsilon = evaluation_epsilon

        if isinstance(self.action_space, BoxActionSpace):
            # for continuous e-greedy (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)
            continuous_exploration_policy_parameters.action_space = action_space
            self.continuous_exploration_policy = \
                dynamic_import_and_instantiate_module_from_params(continuous_exploration_policy_parameters)

        self.current_random_value = np.random.rand()

    def requires_action_values(self):
        epsilon = self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
        return self.current_random_value >= epsilon

    def get_action(self, action_values: List[ActionType]) -> ActionType:
        epsilon = self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value

        if isinstance(self.action_space, DiscreteActionSpace):
            top_action = np.argmax(action_values)
            if self.current_random_value < epsilon:
                chosen_action = self.action_space.sample()
            else:
                chosen_action = top_action
        else:
            if self.current_random_value < epsilon and self.phase == RunPhase.TRAIN:
                chosen_action = self.action_space.sample()
            else:
                chosen_action = self.continuous_exploration_policy.get_action(action_values)

        # step the epsilon schedule and generate a new random value for next time
        if self.phase == RunPhase.TRAIN:
            self.epsilon_schedule.step()
        self.current_random_value = np.random.rand()
        return chosen_action

    def get_control_param(self):
        if isinstance(self.action_space, DiscreteActionSpace):
            return self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
        elif isinstance(self.action_space, BoxActionSpace):
            return self.continuous_exploration_policy.get_control_param()

    def change_phase(self, phase):
        super().change_phase(phase)
        if isinstance(self.action_space, BoxActionSpace):
            self.continuous_exploration_policy.change_phase(phase)