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
110 行
5.2 KiB
110 行
5.2 KiB
# # Unity ML-Agents Toolkit
|
|
|
|
import logging
|
|
|
|
import os
|
|
import multiprocessing
|
|
import numpy as np
|
|
from docopt import docopt
|
|
|
|
from .trainer_controller import TrainerController
|
|
from .exception import TrainerError
|
|
|
|
|
|
def run_training(sub_id, run_seed, run_options):
|
|
"""
|
|
Launches training session.
|
|
:param sub_id: Unique id for training session.
|
|
:param run_seed: Random seed used for training.
|
|
:param run_options: Command line arguments for training.
|
|
"""
|
|
# Docker Parameters
|
|
docker_target_name = (run_options['--docker-target-name']
|
|
if run_options['--docker-target-name'] != 'None' else None)
|
|
|
|
# General parameters
|
|
env_path = (run_options['--env']
|
|
if run_options['--env'] != 'None' else None)
|
|
run_id = run_options['--run-id']
|
|
load_model = run_options['--load']
|
|
train_model = run_options['--train']
|
|
save_freq = int(run_options['--save-freq'])
|
|
keep_checkpoints = int(run_options['--keep-checkpoints'])
|
|
worker_id = int(run_options['--worker-id'])
|
|
curriculum_file = (run_options['--curriculum']
|
|
if run_options['--curriculum'] != 'None' else None)
|
|
lesson = int(run_options['--lesson'])
|
|
fast_simulation = not bool(run_options['--slow'])
|
|
no_graphics = run_options['--no-graphics']
|
|
trainer_config_path = run_options['<trainer-config-path>']
|
|
|
|
# Create controller and begin training.
|
|
tc = TrainerController(env_path, run_id + '-' + str(sub_id),
|
|
save_freq, curriculum_file, fast_simulation,
|
|
load_model, train_model, worker_id + sub_id,
|
|
keep_checkpoints, lesson, run_seed,
|
|
docker_target_name, trainer_config_path, no_graphics)
|
|
tc.start_learning()
|
|
|
|
|
|
def main():
|
|
try:
|
|
print('''
|
|
|
|
▄▄▄▓▓▓▓
|
|
╓▓▓▓▓▓▓█▓▓▓▓▓
|
|
,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
|
|
▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄
|
|
▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
|
|
▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
|
|
▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
|
|
^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
|
|
'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
|
|
▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
|
|
`▀█▓▓▓▓▓▓▓▓▓▌
|
|
¬`▀▀▀█▓
|
|
|
|
''')
|
|
except:
|
|
print('\n\n\tUnity Technologies\n')
|
|
|
|
logger = logging.getLogger('mlagents.trainers')
|
|
_USAGE = '''
|
|
Usage:
|
|
mlagents-learn <trainer-config-path> [options]
|
|
mlagents-learn --help
|
|
|
|
Options:
|
|
--env=<file> Name of the Unity executable [default: None].
|
|
--curriculum=<directory> Curriculum json directory 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 directory name for model and summary statistics [default: ppo].
|
|
--num-runs=<n> Number of concurrent training sessions [default: 1].
|
|
--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) [default: 0].
|
|
--docker-target-name=<dt> Docker volume to store training-specific files [default: None].
|
|
--no-graphics Whether to run the environment in no-graphics mode [default: False].
|
|
'''
|
|
|
|
options = docopt(_USAGE)
|
|
logger.info(options)
|
|
num_runs = int(options['--num-runs'])
|
|
seed = int(options['--seed'])
|
|
|
|
if options['--env'] == 'None' and num_runs > 1:
|
|
raise TrainerError('It is not possible to launch more than one concurrent training session '
|
|
'when training from the editor.')
|
|
|
|
jobs = []
|
|
run_seed = seed
|
|
for i in range(num_runs):
|
|
if seed == -1:
|
|
run_seed = np.random.randint(0, 10000)
|
|
p = multiprocessing.Process(target=run_training, args=(i, run_seed, options))
|
|
jobs.append(p)
|
|
p.start()
|