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524f8436a2
* create per environment Dockerfiles. Adjust CI setup to better parallelize runs. Fix a couple of issues in golden and trace tests. Update a few of the docs. * bugfix in mmc agent. Also install kubectl for CI, update badge branch. * remove integration test parallelism.
75 lines
3.2 KiB
Python
75 lines
3.2 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 DQNAgentParameters, DQNAlgorithmParameters
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from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
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class MixedMonteCarloAlgorithmParameters(DQNAlgorithmParameters):
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def __init__(self):
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super().__init__()
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self.monte_carlo_mixing_rate = 0.1
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class MixedMonteCarloAgentParameters(DQNAgentParameters):
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def __init__(self):
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super().__init__()
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self.algorithm = MixedMonteCarloAlgorithmParameters()
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self.memory = EpisodicExperienceReplayParameters()
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@property
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def path(self):
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return 'rl_coach.agents.mmc_agent:MixedMonteCarloAgent'
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class MixedMonteCarloAgent(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.mixing_rate = agent_parameters.algorithm.monte_carlo_mixing_rate
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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 1-step, we use the double-dqn target. hence actions are taken greedily according to the online network
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selected_actions = np.argmax(self.networks['main'].online_network.predict(batch.next_states(network_keys)), 1)
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# TD_targets are initialized with the current prediction so that we will
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# only update the action that we have actually done in this transition
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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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total_returns = batch.n_step_discounted_rewards()
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for i in range(self.ap.network_wrappers['main'].batch_size):
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one_step_target = batch.rewards()[i] + \
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(1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
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q_st_plus_1[i][selected_actions[i]]
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monte_carlo_target = total_returns[i]
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TD_targets[i, batch.actions()[i]] = (1 - self.mixing_rate) * one_step_target + \
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self.mixing_rate * monte_carlo_target
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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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