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78 lines
3.4 KiB
Python
78 lines
3.4 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.core_types import RunPhase, ActionType
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from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
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from rl_coach.exploration_policies.e_greedy import EGreedy, EGreedyParameters
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from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
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from rl_coach.schedules import Schedule, LinearSchedule
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from rl_coach.spaces import ActionSpace
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class BootstrappedParameters(EGreedyParameters):
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def __init__(self):
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super().__init__()
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self.architecture_num_q_heads = 10
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self.bootstrapped_data_sharing_probability = 1.0
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self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
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@property
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def path(self):
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return 'rl_coach.exploration_policies.bootstrapped:Bootstrapped'
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class Bootstrapped(EGreedy):
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def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule, evaluation_epsilon: float,
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architecture_num_q_heads: int,
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continuous_exploration_policy_parameters: ExplorationParameters = AdditiveNoiseParameters(),):
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"""
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:param action_space: the action space used by the environment
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:param epsilon_schedule: a schedule for the epsilon values
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:param evaluation_epsilon: the epsilon value to use for evaluation phases
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:param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
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if the e-greedy is used for a continuous policy
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:param architecture_num_q_heads: the number of q heads to select from
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"""
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super().__init__(action_space, epsilon_schedule, evaluation_epsilon, continuous_exploration_policy_parameters)
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self.num_heads = architecture_num_q_heads
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self.selected_head = 0
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self.last_action_values = 0
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def select_head(self):
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self.selected_head = np.random.randint(self.num_heads)
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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# action values are none in case the exploration policy is going to select a random action
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if action_values is not None:
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if self.phase == RunPhase.TRAIN:
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action_values = action_values[self.selected_head]
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else:
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# ensemble voting for evaluation
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top_action_votings = np.argmax(action_values, axis=-1)
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counts = np.bincount(top_action_votings.squeeze())
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top_action = np.argmax(counts)
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# convert the top action to a one hot vector and pass it to e-greedy
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action_values = np.eye(len(self.action_space.actions))[[top_action]]
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self.last_action_values = action_values
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return super().get_action(action_values)
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def get_control_param(self):
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return self.selected_head
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