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85 lines
3.7 KiB
Python
85 lines
3.7 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, DQNNetworkParameters
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from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
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from rl_coach.exploration_policies.bootstrapped import BootstrappedParameters
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class BootstrappedDQNNetworkParameters(DQNNetworkParameters):
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def __init__(self):
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super().__init__()
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self.num_output_head_copies = 10
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self.rescale_gradient_from_head_by_factor = [1.0/self.num_output_head_copies]*self.num_output_head_copies
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class BootstrappedDQNAgentParameters(DQNAgentParameters):
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def __init__(self):
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super().__init__()
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self.network_wrappers = {"main": BootstrappedDQNNetworkParameters()}
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self.exploration = BootstrappedParameters()
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@property
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def path(self):
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return 'rl_coach.agents.bootstrapped_dqn_agent:BootstrappedDQNAgent'
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# Bootstrapped DQN - https://arxiv.org/pdf/1602.04621.pdf
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class BootstrappedDQNAgent(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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def reset_internal_state(self):
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super().reset_internal_state()
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self.exploration_policy.select_head()
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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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next_states_online_values = self.networks['main'].online_network.predict(batch.next_states(network_keys))
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result = 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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q_st_plus_1 = result[:self.ap.exploration.architecture_num_q_heads]
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TD_targets = result[self.ap.exploration.architecture_num_q_heads:]
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# initialize 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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for i in range(self.ap.network_wrappers['main'].batch_size):
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mask = batch[i].info['mask']
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for head_idx in range(self.ap.exploration.architecture_num_q_heads):
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if mask[head_idx] == 1:
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selected_action = np.argmax(next_states_online_values[head_idx][i], 0)
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TD_targets[head_idx][i, batch.actions()[i]] = \
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batch.rewards()[i] + (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount \
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* q_st_plus_1[head_idx][i][selected_action]
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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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def observe(self, env_response):
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mask = np.random.binomial(1, self.ap.exploration.bootstrapped_data_sharing_probability,
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self.ap.exploration.architecture_num_q_heads)
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env_response.info['mask'] = mask
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return super().observe(env_response)
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