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https://github.com/gryf/coach.git
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125c7ee38d
Main changes are detailed below: New features - * CARLA 0.7 simulator integration * Human control of the game play * Recording of human game play and storing / loading the replay buffer * Behavioral cloning agent and presets * Golden tests for several presets * Selecting between deep / shallow image embedders * Rendering through pygame (with some boost in performance) API changes - * Improved environment wrapper API * Added an evaluate flag to allow convenient evaluation of existing checkpoints * Improve frameskip definition in Gym Bug fixes - * Fixed loading of checkpoints for agents with more than one network * Fixed the N Step Q learning agent python3 compatibility
71 lines
2.8 KiB
Python
71 lines
2.8 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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from agents.agent import *
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# Imitation Agent
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class ImitationAgent(Agent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.main_network = NetworkWrapper(tuning_parameters, False, 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.imitation = True
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def extract_action_values(self, prediction):
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return prediction.squeeze()
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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.tp.agent.use_measurements:
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measurements = np.expand_dims(np.array(curr_state['measurements']), 0)
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prediction = self.main_network.online_network.predict([observation, measurements])
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else:
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prediction = self.main_network.online_network.predict(observation)
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# get action values and extract the best action from it
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action_values = self.extract_action_values(prediction)
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if self.env.discrete_controls:
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# DISCRETE
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# action = np.argmax(action_values)
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action = self.evaluation_exploration_policy.get_action(action_values)
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action_value = {"action_probability": action_values[action]}
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else:
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# CONTINUOUS
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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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def log_to_screen(self, phase):
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# log to screen
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if phase == RunPhase.TRAIN:
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# for the training phase - we log during the episode to visualize the progress in training
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screen.log_dict(
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OrderedDict([
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("Worker", self.task_id),
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("Episode", self.current_episode),
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("Loss", self.loss.values[-1]),
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("Training iteration", self.training_iteration)
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]),
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prefix="Training"
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)
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else:
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# for the evaluation phase - logging as in regular RL
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Agent.log_to_screen(self, phase)
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