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101 lines
4.5 KiB
Python
101 lines
4.5 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import List
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import numpy as np
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from rl_coach.schedules import Schedule, LinearSchedule
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from scipy.stats import truncnorm
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from rl_coach.spaces import ActionSpace, BoxActionSpace
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from rl_coach.core_types import RunPhase, ActionType
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from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
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class TruncatedNormalParameters(ExplorationParameters):
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def __init__(self):
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super().__init__()
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self.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
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self.evaluation_noise_percentage = 0.05
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self.clip_low = 0
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self.clip_high = 1
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@property
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def path(self):
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return 'rl_coach.exploration_policies.truncated_normal:TruncatedNormal'
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class TruncatedNormal(ExplorationPolicy):
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def __init__(self, action_space: ActionSpace, noise_percentage_schedule: Schedule,
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evaluation_noise_percentage: float, clip_low: float, clip_high: float):
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"""
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:param action_space: the action space used by the environment
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:param noise_percentage_schedule: the schedule for the noise variance percentage relative to the absolute range
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of the action space
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:param evaluation_noise_percentage: the noise variance percentage that will be used during evaluation phases
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"""
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super().__init__(action_space)
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self.noise_percentage_schedule = noise_percentage_schedule
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self.evaluation_noise_percentage = evaluation_noise_percentage
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self.clip_low = clip_low
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self.clip_high = clip_high
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if not isinstance(action_space, BoxActionSpace):
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raise ValueError("Truncated normal exploration works only for continuous controls."
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"The given action space is of type: {}".format(action_space.__class__.__name__))
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if not np.all(-np.inf < action_space.high) or not np.all(action_space.high < np.inf)\
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or not np.all(-np.inf < action_space.low) or not np.all(action_space.low < np.inf):
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raise ValueError("Additive noise exploration requires bounded actions")
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# TODO: allow working with unbounded actions by defining the noise in terms of range and not percentage
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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# set the current noise percentage
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if self.phase == RunPhase.TEST:
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current_noise_precentage = self.evaluation_noise_percentage
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else:
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current_noise_precentage = self.noise_percentage_schedule.current_value
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# scale the noise to the action space range
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action_values_std = current_noise_precentage * (self.action_space.high - self.action_space.low)
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# extract the mean values
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if isinstance(action_values, list):
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# the action values are expected to be a list with the action mean and optionally the action stdev
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action_values_mean = action_values[0].squeeze()
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else:
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# the action values are expected to be a numpy array representing the action mean
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action_values_mean = action_values.squeeze()
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# step the noise schedule
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if self.phase == RunPhase.TRAIN:
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self.noise_percentage_schedule.step()
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# the second element of the list is assumed to be the standard deviation
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if isinstance(action_values, list) and len(action_values) > 1:
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action_values_std = action_values[1].squeeze()
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# sample from truncated normal distribution
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normalized_low = (self.clip_low - action_values_mean) / action_values_std
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normalized_high = (self.clip_high - action_values_mean) / action_values_std
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distribution = truncnorm(normalized_low, normalized_high, loc=action_values_mean, scale=action_values_std)
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action = distribution.rvs(1)
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return action
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def get_control_param(self):
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return np.ones(self.action_space.shape)*self.noise_percentage_schedule.current_value
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