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* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter
132 lines
6.1 KiB
Python
132 lines
6.1 KiB
Python
#
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# Copyright (c) 2019 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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import copy
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from typing import Union
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from collections import OrderedDict
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import numpy as np
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from rl_coach.agents.ddpg_agent import DDPGAlgorithmParameters, DDPGActorNetworkParameters, \
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DDPGCriticNetworkParameters, DDPGAgent
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from rl_coach.base_parameters import AgentParameters
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from rl_coach.core_types import ActionInfo
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from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
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from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
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from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
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from rl_coach.architectures.head_parameters import WolpertingerActorHeadParameters
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class WolpertingerCriticNetworkParameters(DDPGCriticNetworkParameters):
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def __init__(self, use_batchnorm=False):
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super().__init__(use_batchnorm=use_batchnorm)
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class WolpertingerActorNetworkParameters(DDPGActorNetworkParameters):
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def __init__(self, use_batchnorm=False):
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super().__init__()
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self.heads_parameters = [WolpertingerActorHeadParameters(batchnorm=use_batchnorm)]
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class WolpertingerAlgorithmParameters(DDPGAlgorithmParameters):
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def __init__(self):
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super().__init__()
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self.action_embedding_width = 1
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self.k = 1
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class WolpertingerAgentParameters(AgentParameters):
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def __init__(self, use_batchnorm=False):
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exploration_params = AdditiveNoiseParameters()
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exploration_params.noise_as_percentage_from_action_space = False
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super().__init__(algorithm=WolpertingerAlgorithmParameters(),
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exploration=exploration_params,
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memory=EpisodicExperienceReplayParameters(),
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networks=OrderedDict(
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[("actor", WolpertingerActorNetworkParameters(use_batchnorm=use_batchnorm)),
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("critic", WolpertingerCriticNetworkParameters(use_batchnorm=use_batchnorm))]))
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@property
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def path(self):
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return 'rl_coach.agents.wolpertinger_agent:WolpertingerAgent'
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# Deep Reinforcement Learning in Large Discrete Action Spaces - https://arxiv.org/pdf/1512.07679.pdf
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class WolpertingerAgent(DDPGAgent):
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def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent'] = None):
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super().__init__(agent_parameters, parent)
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def learn_from_batch(self, batch):
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# replay buffer holds the actions in the discrete manner, as the agent is expected to act with discrete actions
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# with the BoxDiscretization output filter. But DDPG needs to work on continuous actions, thus converting to
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# continuous actions. This is actually a duplicate since this filtering is also done before applying actions on
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# the environment. So might want to somehow reuse that conversion. Maybe can hold this information in the info
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# dictionary of the transition.
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output_action_filter = \
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list(self.output_filter.action_filters.values())[0]
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continuous_actions = []
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for action in batch.actions():
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continuous_actions.append(output_action_filter.filter(action))
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batch._actions = np.array(continuous_actions).squeeze()
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return super().learn_from_batch(batch)
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def train(self):
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return super().train()
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def choose_action(self, curr_state):
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if not isinstance(self.spaces.action, DiscreteActionSpace):
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raise ValueError("WolpertingerAgent works only for discrete control problems")
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# convert to batch so we can run it through the network
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tf_input_state = self.prepare_batch_for_inference(curr_state, 'actor')
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actor_network = self.networks['actor'].online_network
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critic_network = self.networks['critic'].online_network
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proto_action = actor_network.predict(tf_input_state)
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proto_action = np.expand_dims(self.exploration_policy.get_action(proto_action), 0)
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nn_action_embeddings, indices, _, _ = self.knn_tree.query(keys=proto_action, k=self.ap.algorithm.k)
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# now move the actions through the critic and choose the one with the highest q value
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critic_inputs = copy.copy(tf_input_state)
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critic_inputs['observation'] = np.tile(critic_inputs['observation'], (self.ap.algorithm.k, 1))
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critic_inputs['action'] = nn_action_embeddings[0]
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q_values = critic_network.predict(critic_inputs)[0]
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action = int(indices[0][np.argmax(q_values)])
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self.action_signal.add_sample(action)
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return ActionInfo(action=action, action_value=0)
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def init_environment_dependent_modules(self):
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super().init_environment_dependent_modules()
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self.knn_tree = self.get_initialized_knn()
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# TODO - ideally the knn should not be defined here, but somehow be defined by the user in the preset
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def get_initialized_knn(self):
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num_actions = len(self.spaces.action.actions)
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action_max_abs_range = self.spaces.action.filtered_action_space.max_abs_range if \
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(hasattr(self.spaces.action, 'filtered_action_space') and
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isinstance(self.spaces.action.filtered_action_space, BoxActionSpace)) \
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else 1.0
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keys = np.expand_dims((np.arange(num_actions) / (num_actions - 1) - 0.5) * 2, 1) * action_max_abs_range
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values = np.expand_dims(np.arange(num_actions), 1)
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knn_tree = AnnoyDictionary(dict_size=num_actions, key_width=self.ap.algorithm.action_embedding_width)
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knn_tree.add(keys, values, force_rebuild_tree=True)
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return knn_tree
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