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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
68 lines
2.3 KiB
Python
68 lines
2.3 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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import pygame
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class HumanAgent(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.clock = pygame.time.Clock()
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self.max_fps = int(self.tp.visualization.max_fps_for_human_control)
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screen.log_title("Human Control Mode")
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available_keys = self.env.get_available_keys()
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if available_keys:
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screen.log("Use keyboard keys to move. Press escape to quit. Available keys:")
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screen.log("")
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for action, key in self.env.get_available_keys():
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screen.log("\t- {}: {}".format(action, key))
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screen.separator()
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def train(self):
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return 0
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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action = self.env.get_action_from_user()
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# keep constant fps
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self.clock.tick(self.max_fps)
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if not self.env.renderer.is_open:
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self.save_replay_buffer_and_exit()
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return action, {"action_value": 0}
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def save_replay_buffer_and_exit(self):
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replay_buffer_path = os.path.join(logger.experiments_path, 'replay_buffer.p')
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self.memory.tp = None
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to_pickle(self.memory, replay_buffer_path)
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screen.log_title("Replay buffer was stored in {}".format(replay_buffer_path))
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exit()
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def log_to_screen(self, phase):
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# log to screen
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screen.log_dict(
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OrderedDict([
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("Episode", self.current_episode),
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("total reward", self.total_reward_in_current_episode),
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("steps", self.total_steps_counter)
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]),
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prefix="Recording"
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)
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