Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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# # Unity ML Agents
# ## ML-Agent Learning
import logging
import os
from docopt import docopt
from unitytrainers.trainer_controller import TrainerController
if __name__ == '__main__':
logger = logging.getLogger("unityagents")
_USAGE = '''
Usage:
learn (<env>) [options]
Options:
--help Show this message.
--curriculum=<file> Curriculum json file for environment [default: None].
--keep-checkpoints=<n> How many model checkpoints to keep [default: 5].
--lesson=<n> Start learning from this lesson [default: 0].
--load Whether to load the model or randomly initialize [default: False].
--run-id=<path> The sub-directory name for model and summary statistics [default: ppo].
--save-freq=<n> Frequency at which to save model [default: 50000].
--seed=<n> Random seed used for training [default: -1].
--slow Whether to run the game at training speed [default: False].
--train Whether to train model, or only run inference [default: False].
--worker-id=<n> Number to add to communication port (5005). Used for multi-environment [default: 0].
--docker-target-name=<dt> Docker Volume to store curriculum, executable and model files [default: Empty].
'''
options = docopt(_USAGE)
logger.info(options)
# Docker Parameters
if options['--docker-target-name'] == 'Empty':
docker_target_name = ''
else:
docker_target_name = options['--docker-target-name']
# General parameters
run_id = options['--run-id']
seed = int(options['--seed'])
load_model = options['--load']
train_model = options['--train']
save_freq = int(options['--save-freq'])
env_path = options['<env>']
keep_checkpoints = int(options['--keep-checkpoints'])
worker_id = int(options['--worker-id'])
curriculum_file = str(options['--curriculum'])
if curriculum_file == "None":
curriculum_file = None
lesson = int(options['--lesson'])
fast_simulation = not bool(options['--slow'])
# Constants
# Assumption that this yaml is present in same dir as this file
base_path = os.path.dirname(__file__)
TRAINER_CONFIG_PATH = os.path.abspath(os.path.join(base_path, "trainer_config.yaml"))
tc = TrainerController(env_path, run_id, save_freq, curriculum_file, fast_simulation, load_model, train_model,
worker_id, keep_checkpoints, lesson, seed, docker_target_name, TRAINER_CONFIG_PATH)
tc.start_learning()