#
# Copyright (c) 2017 Intel Corporation
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
#

from typing import List

import numpy as np

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

# TODO: consider renaming to gaussian sampling

def __init__(self):
super().__init__()
self.noise_schedule = LinearSchedule(0.1, 0.1, 50000)
self.evaluation_noise = 0.05
self.noise_as_percentage_from_action_space = True

@property
def path(self):

"""
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_schedule: Schedule,
evaluation_noise: float, noise_as_percentage_from_action_space: bool = True):
"""
:param action_space: the action space used by the environment
:param noise_schedule: the schedule for the noise
:param evaluation_noise: the noise variance that will be used during evaluation phases
:param noise_as_percentage_from_action_space: a bool deciding whether the noise is absolute or as a percentage
from the action space
"""
super().__init__(action_space)
self.noise_schedule = noise_schedule
self.evaluation_noise = evaluation_noise
self.noise_as_percentage_from_action_space = noise_as_percentage_from_action_space

if not isinstance(action_space, BoxActionSpace) and \
(hasattr(action_space, 'filtered_action_space') and not
isinstance(action_space.filtered_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")

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
if self.phase == RunPhase.TEST:
current_noise = self.evaluation_noise
else:
current_noise = self.noise_schedule.current_value

# scale the noise to the action space range
if self.noise_as_percentage_from_action_space:
action_values_std = current_noise * (self.action_space.high - self.action_space.low)
else:
action_values_std = current_noise

# 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_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
if self.phase is not RunPhase.TEST:
action = np.random.normal(action_values_mean, action_values_std)
else:
action = action_values_mean

return np.atleast_1d(action)

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