您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
62 行
2.6 KiB
62 行
2.6 KiB
# # 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()
|