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1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
148 lines
6.8 KiB
Python
148 lines
6.8 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 scipy import signal
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from agents import policy_optimization_agent as poa
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import utils
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import logger
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# Actor Critic - https://arxiv.org/abs/1602.01783
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class ActorCriticAgent(poa.PolicyOptimizationAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network = False):
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poa.PolicyOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id, create_target_network)
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self.last_gradient_update_step_idx = 0
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self.action_advantages = utils.Signal('Advantages')
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self.state_values = utils.Signal('Values')
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self.unclipped_grads = utils.Signal('Grads (unclipped)')
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self.value_loss = utils.Signal('Value Loss')
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self.policy_loss = utils.Signal('Policy Loss')
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self.signals.append(self.action_advantages)
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self.signals.append(self.state_values)
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self.signals.append(self.unclipped_grads)
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self.signals.append(self.value_loss)
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self.signals.append(self.policy_loss)
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# Discounting function used to calculate discounted returns.
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def discount(self, x, gamma):
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return signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]
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def get_general_advantage_estimation_values(self, rewards, values):
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# values contain n+1 elements (t ... t+n+1), rewards contain n elements (t ... t + n)
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bootstrap_extended_rewards = np.array(rewards.tolist() + [values[-1]])
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# Approximation based calculation of GAE (mathematically correct only when Tmax = inf,
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# although in practice works even in much smaller Tmax values, e.g. 20)
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deltas = rewards + self.tp.agent.discount * values[1:] - values[:-1]
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gae = self.discount(deltas, self.tp.agent.discount * self.tp.agent.gae_lambda)
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if self.tp.agent.estimate_value_using_gae:
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discounted_returns = np.expand_dims(gae + values[:-1], -1)
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else:
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discounted_returns = np.expand_dims(np.array(self.discount(bootstrap_extended_rewards,
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self.tp.agent.discount)), 1)[:-1]
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return gae, discounted_returns
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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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result = self.main_network.online_network.predict(current_states)
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current_state_values = result[0]
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self.state_values.add_sample(current_state_values)
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# the targets for the state value estimator
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num_transitions = len(game_overs)
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state_value_head_targets = np.zeros((num_transitions, 1))
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# estimate the advantage function
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action_advantages = np.zeros((num_transitions, 1))
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if self.policy_gradient_rescaler == poa.PolicyGradientRescaler.A_VALUE:
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if game_overs[-1]:
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R = 0
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else:
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R = self.main_network.online_network.predict(utils.last_sample(next_states))[0]
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for i in reversed(range(num_transitions)):
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R = rewards[i] + self.tp.agent.discount * R
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state_value_head_targets[i] = R
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action_advantages[i] = R - current_state_values[i]
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elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.GAE:
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# get bootstraps
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bootstrapped_value = self.main_network.online_network.predict(utils.last_sample(next_states))[0]
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values = np.append(current_state_values, bootstrapped_value)
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if game_overs[-1]:
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values[-1] = 0
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# get general discounted returns table
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gae_values, state_value_head_targets = self.get_general_advantage_estimation_values(rewards, values)
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action_advantages = np.vstack(gae_values)
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else:
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logger.screen.warning("WARNING: The requested policy gradient rescaler is not available")
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action_advantages = action_advantages.squeeze(axis=-1)
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if not self.env.discrete_controls and len(actions.shape) < 2:
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actions = np.expand_dims(actions, -1)
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# train
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result = self.main_network.online_network.accumulate_gradients({**current_states, 'output_1_0': actions},
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[state_value_head_targets, action_advantages])
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# logging
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total_loss, losses, unclipped_grads = result[:3]
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self.action_advantages.add_sample(action_advantages)
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self.unclipped_grads.add_sample(unclipped_grads)
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self.value_loss.add_sample(losses[0])
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self.policy_loss.add_sample(losses[1])
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return total_loss
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def choose_action(self, curr_state, phase=utils.RunPhase.TRAIN):
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# TODO: rename curr_state -> state
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# convert to batch so we can run it through the network
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curr_state = {
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k: np.expand_dims(np.array(curr_state[k]), 0)
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for k in curr_state.keys()
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}
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if self.env.discrete_controls:
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# DISCRETE
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state_value, action_probabilities = self.main_network.online_network.predict(curr_state)
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action_probabilities = action_probabilities.squeeze()
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if phase == utils.RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_probabilities)
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else:
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action = np.argmax(action_probabilities)
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action_info = {"action_probability": action_probabilities[action], "state_value": state_value}
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self.entropy.add_sample(-np.sum(action_probabilities * np.log(action_probabilities + eps)))
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else:
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# CONTINUOUS
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state_value, action_values_mean, action_values_std = self.main_network.online_network.predict(curr_state)
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action_values_mean = action_values_mean.squeeze()
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action_values_std = action_values_std.squeeze()
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if phase == utils.RunPhase.TRAIN:
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action = np.squeeze(np.random.randn(1, self.action_space_size) * action_values_std + action_values_mean)
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else:
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action = action_values_mean
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action_info = {"action_probability": action, "state_value": state_value}
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return action, action_info
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