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6d40ad1650
* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
88 lines
3.9 KiB
Python
88 lines
3.9 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, Dict
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import numpy as np
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from rl_coach.agents.dqn_agent import DQNAgentParameters
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from rl_coach.architectures.tensorflow_components.layers import NoisyNetDense
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from rl_coach.base_parameters import AgentParameters, NetworkParameters
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from rl_coach.spaces import ActionSpace, BoxActionSpace, DiscreteActionSpace
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from rl_coach.core_types import ActionType
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from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
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class ParameterNoiseParameters(ExplorationParameters):
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def __init__(self, agent_params: AgentParameters):
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super().__init__()
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if not isinstance(agent_params, DQNAgentParameters):
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raise ValueError("Currently only DQN variants are supported for using an exploration type of "
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"ParameterNoise.")
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self.network_params = agent_params.network_wrappers
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@property
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def path(self):
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return 'rl_coach.exploration_policies.parameter_noise:ParameterNoise'
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class ParameterNoise(ExplorationPolicy):
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"""
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The ParameterNoise exploration policy is intended for both discrete and continuous action spaces.
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It applies the exploration policy by replacing all the dense network layers with noisy layers.
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The noisy layers have both weight means and weight standard deviations, and for each forward pass of the network
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the weights are sampled from a normal distribution that follows the learned weights mean and standard deviation
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values.
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Warning: currently supported only by DQN variants
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"""
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def __init__(self, network_params: Dict[str, NetworkParameters], action_space: ActionSpace):
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"""
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:param action_space: the action space used by the environment
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"""
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super().__init__(action_space)
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self.network_params = network_params
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self._replace_network_dense_layers()
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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if type(self.action_space) == DiscreteActionSpace:
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return np.argmax(action_values)
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elif type(self.action_space) == BoxActionSpace:
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action_values_mean = action_values[0].squeeze()
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action_values_std = action_values[1].squeeze()
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return np.random.normal(action_values_mean, action_values_std)
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else:
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raise ValueError("ActionSpace type {} is not supported for ParameterNoise.".format(type(self.action_space)))
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def get_control_param(self):
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return 0
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def _replace_network_dense_layers(self):
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# replace the dense type for all the networks components (embedders, mw, heads) with a NoisyNetDense
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# NOTE: we are changing network params in a non-params class (an already instantiated class), this could have
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# been prone to a bug, but since the networks are created very late in the game
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# (after agent.init_environment_dependent()_modules is called) - then we are fine.
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for network_wrapper_params in self.network_params.values():
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for component_params in list(network_wrapper_params.input_embedders_parameters.values()) + \
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[network_wrapper_params.middleware_parameters] + \
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network_wrapper_params.heads_parameters:
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component_params.dense_layer = NoisyNetDense
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