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115 lines
5.0 KiB
Python
115 lines
5.0 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 Union
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import numpy as np
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from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters
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from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
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from rl_coach.architectures.tensorflow_components.heads.categorical_q_head import CategoricalQHeadParameters
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from rl_coach.base_parameters import AgentParameters
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from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
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from rl_coach.schedules import LinearSchedule
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from rl_coach.core_types import StateType
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from rl_coach.exploration_policies.e_greedy import EGreedyParameters
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class CategoricalDQNNetworkParameters(DQNNetworkParameters):
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def __init__(self):
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super().__init__()
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self.heads_parameters = [CategoricalQHeadParameters()]
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class CategoricalDQNAlgorithmParameters(DQNAlgorithmParameters):
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def __init__(self):
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super().__init__()
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self.v_min = -10.0
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self.v_max = 10.0
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self.atoms = 51
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class CategoricalDQNExplorationParameters(EGreedyParameters):
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def __init__(self):
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super().__init__()
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self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
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self.evaluation_epsilon = 0.001
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class CategoricalDQNAgentParameters(AgentParameters):
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def __init__(self):
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super().__init__(algorithm=CategoricalDQNAlgorithmParameters(),
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exploration=CategoricalDQNExplorationParameters(),
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memory=ExperienceReplayParameters(),
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networks={"main": CategoricalDQNNetworkParameters()})
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@property
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def path(self):
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return 'rl_coach.agents.categorical_dqn_agent:CategoricalDQNAgent'
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# Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
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class CategoricalDQNAgent(ValueOptimizationAgent):
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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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self.z_values = np.linspace(self.ap.algorithm.v_min, self.ap.algorithm.v_max, self.ap.algorithm.atoms)
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def distribution_prediction_to_q_values(self, prediction):
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return np.dot(prediction, self.z_values)
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# prediction's format is (batch,actions,atoms)
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def get_all_q_values_for_states(self, states: StateType):
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if self.exploration_policy.requires_action_values():
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prediction = self.get_prediction(states)
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q_values = self.distribution_prediction_to_q_values(prediction)
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else:
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q_values = None
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return q_values
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def learn_from_batch(self, batch):
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network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
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# for the action we actually took, the error is calculated by the atoms distribution
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# for all other actions, the error is 0
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distributed_q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([
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(self.networks['main'].target_network, batch.next_states(network_keys)),
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(self.networks['main'].online_network, batch.states(network_keys))
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])
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# only update the action that we have actually done in this transition
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target_actions = np.argmax(self.distribution_prediction_to_q_values(distributed_q_st_plus_1), axis=1)
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m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size))
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batches = np.arange(self.ap.network_wrappers['main'].batch_size)
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for j in range(self.z_values.size):
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tzj = np.fmax(np.fmin(batch.rewards() +
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(1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j],
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self.z_values[self.z_values.size - 1]),
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self.z_values[0])
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bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
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u = (np.ceil(bj)).astype(int)
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l = (np.floor(bj)).astype(int)
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m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj))
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m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l))
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# total_loss = cross entropy between actual result above and predicted result for the given action
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TD_targets[batches, batch.actions()] = m
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result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets)
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total_loss, losses, unclipped_grads = result[:3]
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return total_loss, losses, unclipped_grads
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