ba2b017820
* add log_path flag to command line utility * Update README with log_path flag * clarify logg and viz docs
251 lines
7.2 KiB
Python
251 lines
7.2 KiB
Python
import sys
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import re
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import multiprocessing
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import os.path as osp
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import gym
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from collections import defaultdict
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import tensorflow as tf
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import numpy as np
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from baselines.common.vec_env import VecFrameStack, VecNormalize, VecEnv
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from baselines.common.vec_env.vec_video_recorder import VecVideoRecorder
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from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, make_env
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from baselines.common.tf_util import get_session
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from baselines import logger
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from importlib import import_module
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try:
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from mpi4py import MPI
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except ImportError:
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MPI = None
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try:
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import pybullet_envs
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except ImportError:
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pybullet_envs = None
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try:
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import roboschool
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except ImportError:
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roboschool = None
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_game_envs = defaultdict(set)
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for env in gym.envs.registry.all():
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# TODO: solve this with regexes
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env_type = env._entry_point.split(':')[0].split('.')[-1]
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_game_envs[env_type].add(env.id)
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# reading benchmark names directly from retro requires
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# importing retro here, and for some reason that crashes tensorflow
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# in ubuntu
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_game_envs['retro'] = {
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'BubbleBobble-Nes',
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'SuperMarioBros-Nes',
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'TwinBee3PokoPokoDaimaou-Nes',
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'SpaceHarrier-Nes',
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'SonicTheHedgehog-Genesis',
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'Vectorman-Genesis',
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'FinalFight-Snes',
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'SpaceInvaders-Snes',
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}
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def train(args, extra_args):
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env_type, env_id = get_env_type(args)
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print('env_type: {}'.format(env_type))
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total_timesteps = int(args.num_timesteps)
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seed = args.seed
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learn = get_learn_function(args.alg)
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alg_kwargs = get_learn_function_defaults(args.alg, env_type)
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alg_kwargs.update(extra_args)
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env = build_env(args)
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if args.save_video_interval != 0:
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env = VecVideoRecorder(env, osp.join(logger.get_dir(), "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length)
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if args.network:
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alg_kwargs['network'] = args.network
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else:
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if alg_kwargs.get('network') is None:
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alg_kwargs['network'] = get_default_network(env_type)
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print('Training {} on {}:{} with arguments \n{}'.format(args.alg, env_type, env_id, alg_kwargs))
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model = learn(
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env=env,
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seed=seed,
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total_timesteps=total_timesteps,
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**alg_kwargs
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)
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return model, env
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def build_env(args):
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ncpu = multiprocessing.cpu_count()
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if sys.platform == 'darwin': ncpu //= 2
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nenv = args.num_env or ncpu
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alg = args.alg
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seed = args.seed
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env_type, env_id = get_env_type(args)
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if env_type in {'atari', 'retro'}:
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if alg == 'deepq':
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env = make_env(env_id, env_type, seed=seed, wrapper_kwargs={'frame_stack': True})
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elif alg == 'trpo_mpi':
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env = make_env(env_id, env_type, seed=seed)
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else:
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frame_stack_size = 4
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env = make_vec_env(env_id, env_type, nenv, seed, gamestate=args.gamestate, reward_scale=args.reward_scale)
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env = VecFrameStack(env, frame_stack_size)
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else:
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config = tf.ConfigProto(allow_soft_placement=True,
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intra_op_parallelism_threads=1,
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inter_op_parallelism_threads=1)
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config.gpu_options.allow_growth = True
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get_session(config=config)
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flatten_dict_observations = alg not in {'her'}
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env = make_vec_env(env_id, env_type, args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations)
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if env_type == 'mujoco':
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env = VecNormalize(env, use_tf=True)
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return env
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def get_env_type(args):
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env_id = args.env
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if args.env_type is not None:
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return args.env_type, env_id
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# Re-parse the gym registry, since we could have new envs since last time.
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for env in gym.envs.registry.all():
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env_type = env._entry_point.split(':')[0].split('.')[-1]
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_game_envs[env_type].add(env.id) # This is a set so add is idempotent
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if env_id in _game_envs.keys():
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env_type = env_id
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env_id = [g for g in _game_envs[env_type]][0]
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else:
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env_type = None
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for g, e in _game_envs.items():
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if env_id in e:
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env_type = g
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break
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if ':' in env_id:
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env_type = re.sub(r':.*', '', env_id)
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assert env_type is not None, 'env_id {} is not recognized in env types'.format(env_id, _game_envs.keys())
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return env_type, env_id
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def get_default_network(env_type):
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if env_type in {'atari', 'retro'}:
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return 'cnn'
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else:
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return 'mlp'
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def get_alg_module(alg, submodule=None):
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submodule = submodule or alg
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try:
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# first try to import the alg module from baselines
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alg_module = import_module('.'.join(['baselines', alg, submodule]))
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except ImportError:
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# then from rl_algs
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alg_module = import_module('.'.join(['rl_' + 'algs', alg, submodule]))
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return alg_module
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def get_learn_function(alg):
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return get_alg_module(alg).learn
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def get_learn_function_defaults(alg, env_type):
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try:
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alg_defaults = get_alg_module(alg, 'defaults')
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kwargs = getattr(alg_defaults, env_type)()
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except (ImportError, AttributeError):
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kwargs = {}
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return kwargs
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def parse_cmdline_kwargs(args):
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'''
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convert a list of '='-spaced command-line arguments to a dictionary, evaluating python objects when possible
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'''
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def parse(v):
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assert isinstance(v, str)
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try:
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return eval(v)
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except (NameError, SyntaxError):
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return v
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return {k: parse(v) for k,v in parse_unknown_args(args).items()}
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def configure_logger(log_path, **kwargs):
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if log_path is not None:
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logger.configure(log_path)
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else:
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logger.configure(**kwargs)
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def main(args):
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# configure logger, disable logging in child MPI processes (with rank > 0)
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arg_parser = common_arg_parser()
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args, unknown_args = arg_parser.parse_known_args(args)
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extra_args = parse_cmdline_kwargs(unknown_args)
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if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
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rank = 0
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configure_logger(args.log_path)
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else:
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rank = MPI.COMM_WORLD.Get_rank()
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configure_logger(args.log_path, format_strs=[])
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model, env = train(args, extra_args)
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if args.save_path is not None and rank == 0:
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save_path = osp.expanduser(args.save_path)
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model.save(save_path)
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if args.play:
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logger.log("Running trained model")
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obs = env.reset()
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state = model.initial_state if hasattr(model, 'initial_state') else None
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dones = np.zeros((1,))
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episode_rew = 0
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while True:
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if state is not None:
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actions, _, state, _ = model.step(obs,S=state, M=dones)
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else:
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actions, _, _, _ = model.step(obs)
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obs, rew, done, _ = env.step(actions)
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episode_rew += rew[0] if isinstance(env, VecEnv) else rew
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env.render()
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done = done.any() if isinstance(done, np.ndarray) else done
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if done:
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print('episode_rew={}'.format(episode_rew))
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episode_rew = 0
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obs = env.reset()
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env.close()
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return model
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if __name__ == '__main__':
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main(sys.argv)
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