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63 行
2.6 KiB
63 行
2.6 KiB
# # Unity ML Agents
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# ## ML-Agent Learning
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import logging
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import os
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from docopt import docopt
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from unitytrainers.trainer_controller import TrainerController
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if __name__ == '__main__':
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logger = logging.getLogger("unityagents")
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_USAGE = '''
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Usage:
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learn (<env>) [options]
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execute with --help to get more information
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Options:
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--help Show this message.
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--curriculum=<file> Curriculum json file for environment [default: None].
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--keep-checkpoints=<n> How many model checkpoints to keep [default: 5].
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--lesson=<n> Start learning from this lesson [default: 0].
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--load Whether to load the model or randomly initialize [default: False].
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--run-id=<path> The sub-directory name for model and summary statistics [default: ppo].
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--save-freq=<n> Frequency at which to save model [default: 50000].
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--seed=<n> Random seed used for training [default: -1].
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--slow Whether to run the game at training speed [default: False].
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--train Whether to train model, or only run inference [default: False].
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--worker-id=<n> Number to add to communication port (5005). Used for multi-environment [default: 0].
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--docker-target-name=<dt> Docker Volume to store curriculum, executable and model files [default: Empty].
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'''
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options = docopt(_USAGE)
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logger.info(options)
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# Docker Parameters
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if options['--docker-target-name'] == 'Empty':
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docker_target_name = ''
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else:
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docker_target_name = options['--docker-target-name']
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# General parameters
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run_id = options['--run-id']
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seed = int(options['--seed'])
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load_model = options['--load']
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train_model = options['--train']
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save_freq = int(options['--save-freq'])
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env_path = options['<env>']
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keep_checkpoints = int(options['--keep-checkpoints'])
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worker_id = int(options['--worker-id'])
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curriculum_file = str(options['--curriculum'])
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if curriculum_file == "None":
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curriculum_file = None
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lesson = int(options['--lesson'])
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fast_simulation = not bool(options['--slow'])
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# Constants
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# Assumption that this yaml is present in same dir as this file
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base_path = os.path.dirname(__file__)
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TRAINER_CONFIG_PATH = os.path.abspath(os.path.join(base_path, "trainer_config.yaml"))
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tc = TrainerController(env_path, run_id, save_freq, curriculum_file, fast_simulation, load_model, train_model,
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worker_id, keep_checkpoints, lesson, seed, docker_target_name, TRAINER_CONFIG_PATH)
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tc.start_learning()
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