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86 lines
3.5 KiB
Python
86 lines
3.5 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 agents.value_optimization_agent import *
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from agents.policy_optimization_agent import *
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from logger import *
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from utils import *
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import scipy.signal
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# N Step Q Learning Agent - https://arxiv.org/abs/1602.01783
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class NStepQAgent(ValueOptimizationAgent, PolicyOptimizationAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id, create_target_network=True)
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self.last_gradient_update_step_idx = 0
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self.q_values = Signal('Q Values')
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self.unclipped_grads = Signal('Grads (unclipped)')
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self.signals.append(self.q_values)
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self.signals.append(self.unclipped_grads)
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def learn_from_batch(self, batch):
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# batch contains a list of episodes to learn from
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current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
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# get the values for the current states
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state_value_head_targets = self.main_network.online_network.predict(current_states)
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# the targets for the state value estimator
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num_transitions = len(game_overs)
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if self.tp.agent.targets_horizon == '1-Step':
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# 1-Step Q learning
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q_st_plus_1 = self.main_network.target_network.predict(next_states)
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for i in reversed(xrange(num_transitions)):
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state_value_head_targets[i][actions[i]] = \
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rewards[i] + (1.0 - game_overs[i]) * self.tp.agent.discount * np.max(q_st_plus_1[i], 0)
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elif self.tp.agent.targets_horizon == 'N-Step':
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# N-Step Q learning
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if game_overs[-1]:
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R = 0
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else:
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R = np.max(self.main_network.target_network.predict(np.expand_dims(next_states[-1], 0)))
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for i in reversed(xrange(num_transitions)):
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R = rewards[i] + self.tp.agent.discount * R
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state_value_head_targets[i][actions[i]] = R
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else:
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assert True, 'The available values for targets_horizon are: 1-Step, N-Step'
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# train
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result = self.main_network.online_network.accumulate_gradients([current_states], [state_value_head_targets])
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# logging
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total_loss, losses, unclipped_grads = result[:3]
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self.unclipped_grads.add_sample(unclipped_grads)
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logger.create_signal_value('Value Loss', losses[0])
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return total_loss
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def train(self):
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# update the target network of every network that has a target network
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if self.total_steps_counter % self.tp.agent.num_steps_between_copying_online_weights_to_target == 0:
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for network in self.networks:
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network.update_target_network(self.tp.agent.rate_for_copying_weights_to_target)
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logger.create_signal_value('Update Target Network', 1)
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else:
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logger.create_signal_value('Update Target Network', 0, overwrite=False)
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return PolicyOptimizationAgent.train(self)
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