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95 lines
4.1 KiB
Python
95 lines
4.1 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.policy_optimization_agent import *
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import numpy as np
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from logger import *
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import tensorflow as tf
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try:
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import matplotlib.pyplot as plt
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except:
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from logger import failed_imports
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failed_imports.append("matplotlib")
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from utils import *
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class PolicyGradientsAgent(PolicyOptimizationAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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PolicyOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.returns_mean = Signal('Returns Mean')
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self.returns_variance = Signal('Returns Variance')
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self.signals.append(self.returns_mean)
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self.signals.append(self.returns_variance)
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self.last_gradient_update_step_idx = 0
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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, total_returns = self.extract_batch(batch)
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for i in reversed(range(len(total_returns))):
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if self.policy_gradient_rescaler == PolicyGradientRescaler.TOTAL_RETURN:
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total_returns[i] = total_returns[0]
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elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN:
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# just take the total return as it is
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pass
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elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE:
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# we can get a single transition episode while playing Doom Basic, causing the std to be 0
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if self.std_discounted_return != 0:
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total_returns[i] = (total_returns[i] - self.mean_discounted_return) / self.std_discounted_return
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else:
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total_returns[i] = 0
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elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP:
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total_returns[i] -= self.mean_return_over_multiple_episodes[i]
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else:
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screen.warning("WARNING: The requested policy gradient rescaler is not available")
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targets = total_returns
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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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self.returns_mean.add_sample(np.mean(total_returns))
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self.returns_variance.add_sample(np.std(total_returns))
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result = self.main_network.online_network.accumulate_gradients([current_states, actions], targets)
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total_loss = result[0]
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return total_loss
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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# convert to batch so we can run it through the network
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observation = np.expand_dims(np.array(curr_state['observation']), 0)
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if self.env.discrete_controls:
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# DISCRETE
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action_values = self.main_network.online_network.predict(observation).squeeze()
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if phase == RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_values)
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else:
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action = np.argmax(action_values)
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action_value = {"action_probability": action_values[action]}
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self.entropy.add_sample(-np.sum(action_values * np.log(action_values + eps)))
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else:
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# CONTINUOUS
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result = self.main_network.online_network.predict(observation)
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action_values = result[0].squeeze()
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if phase == RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_values)
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else:
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action = action_values
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action_value = {}
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return action, action_value
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