# rl_coach.exploration_policies.additive_noise — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.exploration_policies.additive_noise

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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
    #
    #      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 List

    import numpy as np

    from rl_coach.core_types import RunPhase, ActionType
    from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
    from rl_coach.schedules import Schedule, LinearSchedule
    from rl_coach.spaces import ActionSpace, BoxActionSpace

    # TODO: consider renaming to gaussian sampling
    class AdditiveNoiseParameters(ExplorationParameters):
        def __init__(self):
            super().__init__()
            self.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
            self.evaluation_noise_percentage = 0.05

        @property
        def path(self):
            return 'rl_coach.exploration_policies.additive_noise:AdditiveNoise'

    class AdditiveNoise(ExplorationPolicy):
        """
        AdditiveNoise is an exploration policy intended for continuous action spaces. It takes the action from the agent
        and adds a Gaussian distributed noise to it. The amount of noise added to the action follows the noise amount that
        can be given in two different ways:
        1. Specified by the user as a noise schedule which is taken in percentiles out of the action space size
        2. Specified by the agents action. In case the agents action is a list with 2 values, the 1st one is assumed to
        be the mean of the action, and 2nd is assumed to be its standard deviation.
        """
        def __init__(self, action_space: ActionSpace, noise_percentage_schedule: Schedule,
                     evaluation_noise_percentage: float):
            """
            :param action_space: the action space used by the environment
            :param noise_percentage_schedule: the schedule for the noise variance percentage relative to the absolute range
                                              of the action space
            :param evaluation_noise_percentage: the noise variance percentage that will be used during evaluation phases
            """
            super().__init__(action_space)
            self.noise_percentage_schedule = noise_percentage_schedule
            self.evaluation_noise_percentage = evaluation_noise_percentage

            if not isinstance(action_space, BoxActionSpace):
                raise ValueError("Additive noise exploration works only for continuous controls."
                                 "The given action space is of type: {}".format(action_space.__class__.__name__))

            if not np.all(-np.inf < action_space.high) or not np.all(action_space.high < np.inf)\
                    or not np.all(-np.inf < action_space.low) or not np.all(action_space.low < np.inf):
                raise ValueError("Additive noise exploration requires bounded actions")

            # TODO: allow working with unbounded actions by defining the noise in terms of range and not percentage

        def get_action(self, action_values: List[ActionType]) -> ActionType:
            # TODO-potential-bug consider separating internally defined stdev and externally defined stdev into 2 policies

            # set the current noise percentage
            if self.phase == RunPhase.TEST:
                current_noise_precentage = self.evaluation_noise_percentage
            else:
                current_noise_precentage = self.noise_percentage_schedule.current_value

            # scale the noise to the action space range
            action_values_std = current_noise_precentage * (self.action_space.high - self.action_space.low)

            # extract the mean values
            if isinstance(action_values, list):
                # the action values are expected to be a list with the action mean and optionally the action stdev
                action_values_mean = action_values[0].squeeze()
            else:
                # the action values are expected to be a numpy array representing the action mean
                action_values_mean = action_values.squeeze()

            # step the noise schedule
            if self.phase is not RunPhase.TEST:
                self.noise_percentage_schedule.step()
                # the second element of the list is assumed to be the standard deviation
                if isinstance(action_values, list) and len(action_values) > 1:
                    action_values_std = action_values[1].squeeze()

            # add noise to the action means
            action = np.random.normal(action_values_mean, action_values_std)

            return action

        def get_control_param(self):
            return np.ones(self.action_space.shape)*self.noise_percentage_schedule.current_value