# # Unity ML-Agents Toolkit import logging import argparse from multiprocessing import Process, Queue import os import glob import shutil import numpy as np from typing import Any, Callable, Optional, List, NamedTuple from mlagents.trainers.trainer_controller import TrainerController from mlagents.trainers.exception import TrainerError from mlagents.trainers.meta_curriculum import MetaCurriculumError, MetaCurriculum from mlagents.trainers.trainer_util import initialize_trainers, load_config from mlagents.envs.environment import UnityEnvironment from mlagents.envs.sampler_class import SamplerManager from mlagents.envs.exception import SamplerException from mlagents.envs.base_unity_environment import BaseUnityEnvironment from mlagents.envs.subprocess_env_manager import SubprocessEnvManager class CommandLineOptions(NamedTuple): debug: bool num_runs: int seed: int env_path: str run_id: str load_model: bool train_model: bool save_freq: int keep_checkpoints: int base_port: int num_envs: int curriculum_folder: Optional[str] lesson: int slow: bool no_graphics: bool multi_gpu: bool # ? trainer_config_path: str sampler_file_path: Optional[str] docker_target_name: Optional[str] env_args: Optional[List[str]] @property def fast_simulation(self) -> bool: return not self.slow @staticmethod def from_argparse(args: Any) -> "CommandLineOptions": return CommandLineOptions(**vars(args)) def parse_command_line(argv: Optional[List[str]] = None) -> CommandLineOptions: parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("trainer_config_path") parser.add_argument( "--env", default=None, dest="env_path", help="Name of the Unity executable " ) parser.add_argument( "--curriculum", default=None, dest="curriculum_folder", help="Curriculum json directory for environment", ) parser.add_argument( "--sampler", default=None, dest="sampler_file_path", help="Reset parameter yaml file for environment", ) parser.add_argument( "--keep-checkpoints", default=5, type=int, help="How many model checkpoints to keep", ) parser.add_argument( "--lesson", default=0, type=int, help="Start learning from this lesson" ) parser.add_argument( "--load", default=False, dest="load_model", action="store_true", help="Whether to load the model or randomly initialize", ) parser.add_argument( "--run-id", default="ppo", help="The directory name for model and summary statistics", ) parser.add_argument( "--num-runs", default=1, type=int, help="Number of concurrent training sessions" ) parser.add_argument( "--save-freq", default=50000, type=int, help="Frequency at which to save model" ) parser.add_argument( "--seed", default=-1, type=int, help="Random seed used for training" ) parser.add_argument( "--slow", action="store_true", help="Whether to run the game at training speed" ) parser.add_argument( "--train", default=False, dest="train_model", action="store_true", help="Whether to train model, or only run inference", ) parser.add_argument( "--base-port", default=5005, type=int, help="Base port for environment communication", ) parser.add_argument( "--num-envs", default=1, type=int, help="Number of parallel environments to use for training", ) parser.add_argument( "--docker-target-name", default=None, dest="docker_target_name", help="Docker volume to store training-specific files", ) parser.add_argument( "--no-graphics", default=False, action="store_true", help="Whether to run the environment in no-graphics mode", ) parser.add_argument( "--debug", default=False, action="store_true", help="Whether to run ML-Agents in debug mode with detailed logging", ) parser.add_argument( "--multi-gpu", default=False, action="store_true", help="Setting this flag enables the use of multiple GPU's (if available) during training", ) parser.add_argument( "--env-args", default=None, nargs=argparse.REMAINDER, help="Arguments passed to the Unity executable.", ) args = parser.parse_args(argv) return CommandLineOptions.from_argparse(args) def run_training( sub_id: int, run_seed: int, options: CommandLineOptions, process_queue: Queue ) -> None: """ Launches training session. :param process_queue: Queue used to send signal back to main. :param sub_id: Unique id for training session. :param options: parsed command line arguments :param run_seed: Random seed used for training. :param run_options: Command line arguments for training. """ # Docker Parameters trainer_config_path = options.trainer_config_path curriculum_folder = options.curriculum_folder # Recognize and use docker volume if one is passed as an argument if not options.docker_target_name: model_path = "./models/{run_id}-{sub_id}".format( run_id=options.run_id, sub_id=sub_id ) summaries_dir = "./summaries" else: trainer_config_path = "/{docker_target_name}/{trainer_config_path}".format( docker_target_name=options.docker_target_name, trainer_config_path=trainer_config_path, ) if curriculum_folder is not None: curriculum_folder = "/{docker_target_name}/{curriculum_folder}".format( docker_target_name=options.docker_target_name, curriculum_folder=curriculum_folder, ) model_path = "/{docker_target_name}/models/{run_id}-{sub_id}".format( docker_target_name=options.docker_target_name, run_id=options.run_id, sub_id=sub_id, ) summaries_dir = "/{docker_target_name}/summaries".format( docker_target_name=options.docker_target_name ) trainer_config = load_config(trainer_config_path) env_factory = create_environment_factory( options.env_path, options.docker_target_name, options.no_graphics, run_seed, options.base_port + (sub_id * options.num_envs), options.env_args, ) env = SubprocessEnvManager(env_factory, options.num_envs) maybe_meta_curriculum = try_create_meta_curriculum( curriculum_folder, env, options.lesson ) sampler_manager, resampling_interval = create_sampler_manager( options.sampler_file_path, env.reset_parameters, run_seed ) trainers = initialize_trainers( trainer_config, env.external_brains, summaries_dir, options.run_id, model_path, options.keep_checkpoints, options.train_model, options.load_model, run_seed, maybe_meta_curriculum, options.multi_gpu, ) # Create controller and begin training. tc = TrainerController( trainers, model_path, summaries_dir, options.run_id + "-" + str(sub_id), options.save_freq, maybe_meta_curriculum, options.train_model, run_seed, options.fast_simulation, sampler_manager, resampling_interval, ) # Signal that environment has been launched. process_queue.put(True) # Begin training tc.start_learning(env) def create_sampler_manager(sampler_file_path, env_reset_params, run_seed=None): sampler_config = None resample_interval = None if sampler_file_path is not None: sampler_config = load_config(sampler_file_path) if "resampling-interval" in sampler_config: # Filter arguments that do not exist in the environment resample_interval = sampler_config.pop("resampling-interval") if (resample_interval <= 0) or (not isinstance(resample_interval, int)): raise SamplerException( "Specified resampling-interval is not valid. Please provide" " a positive integer value for resampling-interval" ) else: raise SamplerException( "Resampling interval was not specified in the sampler file." " Please specify it with the 'resampling-interval' key in the sampler config file." ) sampler_manager = SamplerManager(sampler_config, run_seed) return sampler_manager, resample_interval def try_create_meta_curriculum( curriculum_folder: Optional[str], env: SubprocessEnvManager, lesson: int ) -> Optional[MetaCurriculum]: if curriculum_folder is None: return None else: meta_curriculum = MetaCurriculum(curriculum_folder, env.reset_parameters) # TODO: Should be able to start learning at different lesson numbers # for each curriculum. meta_curriculum.set_all_curriculums_to_lesson_num(lesson) for brain_name in meta_curriculum.brains_to_curriculums.keys(): if brain_name not in env.external_brains.keys(): raise MetaCurriculumError( "One of the curricula " "defined in " + curriculum_folder + " " "does not have a corresponding " "Brain. Check that the " "curriculum file has the same " "name as the Brain " "whose curriculum it defines." ) return meta_curriculum def prepare_for_docker_run(docker_target_name, env_path): for f in glob.glob( "/{docker_target_name}/*".format(docker_target_name=docker_target_name) ): if env_path in f: try: b = os.path.basename(f) if os.path.isdir(f): shutil.copytree(f, "/ml-agents/{b}".format(b=b)) else: src_f = "/{docker_target_name}/{b}".format( docker_target_name=docker_target_name, b=b ) dst_f = "/ml-agents/{b}".format(b=b) shutil.copyfile(src_f, dst_f) os.chmod(dst_f, 0o775) # Make executable except Exception as e: logging.getLogger("mlagents.trainers").info(e) env_path = "/ml-agents/{env_path}".format(env_path=env_path) return env_path def create_environment_factory( env_path: str, docker_target_name: Optional[str], no_graphics: bool, seed: Optional[int], start_port: int, env_args: Optional[List[str]], ) -> Callable[[int], BaseUnityEnvironment]: if env_path is not None: # Strip out executable extensions if passed env_path = ( env_path.strip() .replace(".app", "") .replace(".exe", "") .replace(".x86_64", "") .replace(".x86", "") ) docker_training = docker_target_name is not None if docker_training and env_path is not None: """ Comments for future maintenance: Some OS/VM instances (e.g. COS GCP Image) mount filesystems with COS flag which prevents execution of the Unity scene, to get around this, we will copy the executable into the container. """ # Navigate in docker path and find env_path and copy it. env_path = prepare_for_docker_run(docker_target_name, env_path) seed_count = 10000 seed_pool = [np.random.randint(0, seed_count) for _ in range(seed_count)] def create_unity_environment(worker_id: int) -> UnityEnvironment: env_seed = seed if not env_seed: env_seed = seed_pool[worker_id % len(seed_pool)] return UnityEnvironment( file_name=env_path, worker_id=worker_id, seed=env_seed, docker_training=docker_training, no_graphics=no_graphics, base_port=start_port, args=env_args, ) return create_unity_environment def main(): try: print( """ ▄▄▄▓▓▓▓ ╓▓▓▓▓▓▓█▓▓▓▓▓ ,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌ ▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄ ▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌ ▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌ ▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓ ^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓` '▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌ ▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀ `▀█▓▓▓▓▓▓▓▓▓▌ ¬`▀▀▀█▓ """ ) except Exception: print("\n\n\tUnity Technologies\n") options = parse_command_line() trainer_logger = logging.getLogger("mlagents.trainers") env_logger = logging.getLogger("mlagents.envs") trainer_logger.info(options) if options.debug: trainer_logger.setLevel("DEBUG") env_logger.setLevel("DEBUG") if options.env_path is None and options.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 = options.seed if options.num_runs == 1: if options.seed == -1: run_seed = np.random.randint(0, 10000) run_training(0, run_seed, options, Queue()) else: for i in range(options.num_runs): if options.seed == -1: run_seed = np.random.randint(0, 10000) process_queue = Queue() p = Process(target=run_training, args=(i, run_seed, options, process_queue)) jobs.append(p) p.start() # Wait for signal that environment has successfully launched while process_queue.get() is not True: continue # Wait for jobs to complete. Otherwise we'll have an extra # unhandled KeyboardInterrupt if we end early. try: for job in jobs: job.join() except KeyboardInterrupt: pass # For python debugger to directly run this script if __name__ == "__main__": main()