mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
80 lines
3.4 KiB
Python
80 lines
3.4 KiB
Python
#
|
|
# Copyright (c) 2017 Intel Corporation
|
|
#
|
|
# 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.
|
|
#
|
|
|
|
from typing import List, Dict
|
|
|
|
import numpy as np
|
|
|
|
from rl_coach.agents.dqn_agent import DQNAgentParameters
|
|
from rl_coach.architectures.tensorflow_components.layers import NoisyNetDense
|
|
from rl_coach.base_parameters import AgentParameters, NetworkParameters
|
|
from rl_coach.spaces import ActionSpace, BoxActionSpace, DiscreteActionSpace
|
|
|
|
from rl_coach.core_types import ActionType
|
|
from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
|
|
|
|
|
|
class ParameterNoiseParameters(ExplorationParameters):
|
|
def __init__(self, agent_params: AgentParameters):
|
|
super().__init__()
|
|
if not isinstance(agent_params, DQNAgentParameters):
|
|
raise ValueError("Currently only DQN variants are supported for using an exploration type of "
|
|
"ParameterNoise.")
|
|
|
|
self.network_params = agent_params.network_wrappers
|
|
|
|
@property
|
|
def path(self):
|
|
return 'rl_coach.exploration_policies.parameter_noise:ParameterNoise'
|
|
|
|
|
|
class ParameterNoise(ExplorationPolicy):
|
|
def __init__(self, network_params: Dict[str, NetworkParameters], action_space: ActionSpace):
|
|
"""
|
|
:param action_space: the action space used by the environment
|
|
:param alpha0:
|
|
"""
|
|
super().__init__(action_space)
|
|
self.network_params = network_params
|
|
self._replace_network_dense_layers()
|
|
|
|
def get_action(self, action_values: List[ActionType]) -> ActionType:
|
|
if type(self.action_space) == DiscreteActionSpace:
|
|
return np.argmax(action_values)
|
|
elif type(self.action_space) == BoxActionSpace:
|
|
action_values_mean = action_values[0].squeeze()
|
|
action_values_std = action_values[1].squeeze()
|
|
return np.random.normal(action_values_mean, action_values_std)
|
|
else:
|
|
raise ValueError("ActionSpace type {} is not supported for ParameterNoise.".format(type(self.action_space)))
|
|
|
|
def get_control_param(self):
|
|
return 0
|
|
|
|
def _replace_network_dense_layers(self):
|
|
# replace the dense type for all the networks components (embedders, mw, heads) with a NoisyNetDense
|
|
|
|
# NOTE: we are changing network params in a non-params class (an already instantiated class), this could have
|
|
# been prone to a bug, but since the networks are created very late in the game
|
|
# (after agent.init_environment_dependent()_modules is called) - then we are fine.
|
|
|
|
for network_wrapper_params in self.network_params.values():
|
|
for component_params in list(network_wrapper_params.input_embedders_parameters.values()) + \
|
|
[network_wrapper_params.middleware_parameters] + \
|
|
network_wrapper_params.heads_parameters:
|
|
component_params.dense_layer = NoisyNetDense
|
|
|