mirror of
https://github.com/gryf/coach.git
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a7206ed702
* Multiple improvements and bug fixes:
* Using lazy stacking to save on memory when using a replay buffer
* Remove step counting for evaluation episodes
* Reset game between heatup and training
* Major bug fixes in NEC (is reproducing the paper results for pong now)
* Image input rescaling to 0-1 is now optional
* Change the terminal title to be the experiment name
* Observation cropping for atari is now optional
* Added random number of noop actions for gym to match the dqn paper
* Fixed a bug where the evaluation episodes won't start with the max possible ale lives
* Added a script for plotting the results of an experiment over all the atari games
78 lines
3.4 KiB
Python
78 lines
3.4 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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import numpy as np
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from agents.agent import Agent
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from architectures.network_wrapper import NetworkWrapper
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from utils import RunPhase, Signal
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class ValueOptimizationAgent(Agent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network=True):
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Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.main_network = NetworkWrapper(tuning_parameters, create_target_network, self.has_global, 'main',
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self.replicated_device, self.worker_device)
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self.networks.append(self.main_network)
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self.q_values = Signal("Q")
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self.signals.append(self.q_values)
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self.reset_game(do_not_reset_env=True)
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# Algorithms for which q_values are calculated from predictions will override this function
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def get_q_values(self, prediction):
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return prediction
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def get_prediction(self, curr_state):
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return self.main_network.online_network.predict(self.tf_input_state(curr_state))
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def _validate_action(self, policy, action):
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if np.array(action).shape != ():
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raise ValueError((
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'The exploration_policy {} returned a vector of actions '
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'instead of a single action. ValueOptimizationAgents '
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'require exploration policies which return a single action.'
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).format(policy.__class__.__name__))
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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prediction = self.get_prediction(curr_state)
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actions_q_values = self.get_q_values(prediction)
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# choose action according to the exploration policy and the current phase (evaluating or training the agent)
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if phase == RunPhase.TRAIN:
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exploration_policy = self.exploration_policy
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else:
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exploration_policy = self.evaluation_exploration_policy
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action = exploration_policy.get_action(actions_q_values)
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self._validate_action(exploration_policy, action)
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# this is for bootstrapped dqn
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if type(actions_q_values) == list and len(actions_q_values) > 0:
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actions_q_values = actions_q_values[self.exploration_policy.selected_head]
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actions_q_values = actions_q_values.squeeze()
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# store the q values statistics for logging
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self.q_values.add_sample(actions_q_values)
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# store information for plotting interactively (actual plotting is done in agent)
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if self.tp.visualization.plot_action_values_online:
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for idx, action_name in enumerate(self.env.actions_description):
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self.episode_running_info[action_name].append(actions_q_values[idx])
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action_value = {"action_value": actions_q_values[action], "max_action_value": np.max(actions_q_values)}
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return action, action_value
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