mirror of
https://github.com/gryf/coach.git
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310d31c227
* integration test changes to override heatup to 1000 steps + run each preset for 30 sec (to make sure we reach the train part) * fixes to failing presets uncovered with this change + changes in the golden testing to properly test BatchRL * fix for rainbow dqn * fix to gym_environment (due to a change in Gym 0.12.1) + fix for rainbow DQN + some bug-fix in utils.squeeze_list * fix for NEC agent
159 lines
7.1 KiB
Python
159 lines
7.1 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, DQNAgentParameters
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from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
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from rl_coach.architectures.head_parameters import CategoricalQHeadParameters
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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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from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
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from rl_coach.schedules import LinearSchedule
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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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"""
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:param v_min: (float)
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The minimal value that will be represented in the network output for predicting the Q value.
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Corresponds to :math:`v_{min}` in the paper.
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:param v_max: (float)
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The maximum value that will be represented in the network output for predicting the Q value.
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Corresponds to :math:`v_{max}` in the paper.
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:param atoms: (int)
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The number of atoms that will be used to discretize the range between v_min and v_max.
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For the C51 algorithm described in the paper, the number of atoms is 51.
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"""
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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(DQNAgentParameters):
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def __init__(self):
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super().__init__()
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self.algorithm = CategoricalDQNAlgorithmParameters()
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self.exploration = CategoricalDQNExplorationParameters()
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self.network_wrappers = {"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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q_values = self.get_prediction(states,
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outputs=[self.networks['main'].online_network.output_heads[0].q_values])
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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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distributional_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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# add Q value samples for logging
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self.q_values.add_sample(self.distribution_prediction_to_q_values(TD_targets))
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# select the optimal actions for the next state
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target_actions = np.argmax(self.distribution_prediction_to_q_values(distributional_q_st_plus_1), axis=1)
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m = np.zeros((batch.size, self.z_values.size))
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batches = np.arange(batch.size)
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# an alternative to the for loop. 3.7x perf improvement vs. the same code done with for looping.
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# only 10% speedup overall - leaving commented out as the code is not as clear.
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# tzj_ = np.fmax(np.fmin(batch.rewards() + (1.0 - batch.game_overs()) * self.ap.algorithm.discount *
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# np.transpose(np.repeat(self.z_values[np.newaxis, :], batch.size, axis=0), (1, 0)),
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# self.z_values[-1]),
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# self.z_values[0])
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#
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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_ = np.zeros((batch.size, self.z_values.size))
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# np.add.at(m_, [batches, l_],
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# np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (u_ - bj_))
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# np.add.at(m_, [batches, u_],
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# np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (bj_ - l_))
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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[-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] += (distributional_q_st_plus_1[batches, target_actions, j] * (u - bj))
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m[batches, u] += (distributional_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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# only update the action that we have actually done in this transition
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TD_targets[batches, batch.actions()] = m
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# update errors in prioritized replay buffer
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importance_weights = batch.info('weight') if isinstance(self.memory, PrioritizedExperienceReplay) else None
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result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
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importance_weights=importance_weights)
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total_loss, losses, unclipped_grads = result[:3]
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# TODO: fix this spaghetti code
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if isinstance(self.memory, PrioritizedExperienceReplay):
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errors = losses[0][np.arange(batch.size), batch.actions()]
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self.call_memory('update_priorities', (batch.info('idx'), errors))
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return total_loss, losses, unclipped_grads
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