learn.py

# # Unity ML-Agents Toolkit
# ## 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]
      learn [options]
      learn --help

    Options:
      --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].
      --no-graphics              Whether to run the Unity simulator in no-graphics mode [default: False].
      --trainer-config-path=<path>      Training config yaml [default: False].
    '''

    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'])
    no_graphics = options['--no-graphics']
    trainer_config_path = options['--trainer-config-path']

    if not trainer_config_path:
        # Assumption that this yaml is present in same dir as this file
        base_path = os.path.dirname(__file__)
        trainer_config_path = trainer_config_path or 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,
                           no_graphics)
    tc.start_learning()