baselines/baselines/run.py

import sys
import multiprocessing
import os.path as osp
import gym
from collections import defaultdict
import numpy as np

from baselines.common.vec_env.vec_normalize import VecNormalize
from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, env_thunk
from baselines import logger
from baselines.registry import registry

try:
    from mpi4py import MPI
except ImportError:
    MPI = None

try:
    import pybullet_envs
except ImportError:
    pybullet_envs = None

try:
    import roboschool
except ImportError:
    roboschool = None

_game_envs = defaultdict(set)
for env in gym.envs.registry.all():
    # TODO: solve this with regexes
    env_type = env._entry_point.split(':')[0].split('.')[-1]
    _game_envs[env_type].add(env.id)

# reading benchmark names directly from retro requires
# importing retro here, and for some reason that crashes tensorflow
# in ubuntu
_game_envs['retro'] = {
    'BubbleBobble-Nes',
    'SuperMarioBros-Nes',
    'TwinBee3PokoPokoDaimaou-Nes',
    'SpaceHarrier-Nes',
    'SonicTheHedgehog-Genesis',
    'Vectorman-Genesis',
    'FinalFight-Snes',
    'SpaceInvaders-Snes',
}


def train(args, extra_args):
    env_type, env_id = get_env_type(args.env)
    print('env_type: {}'.format(env_type))

    total_timesteps = int(args.num_timesteps)
    seed = args.seed

    learn = get_learn_function(args.alg)
    alg_kwargs = get_learn_function_defaults(args.alg, env_type)
    alg_kwargs.update(extra_args)

    env = build_env(args)

    if args.network:
        alg_kwargs['network'] = args.network
    else:
        if alg_kwargs.get('network') is None:
            alg_kwargs['network'] = get_default_network(env_type)

    print('Training {} on {}:{} with arguments \n{}'.format(args.alg, env_type, env_id, alg_kwargs))

    model = learn(
        env=env,
        seed=seed,
        total_timesteps=total_timesteps,
        **alg_kwargs
    )

    return model, env


def build_env(args):
    ncpu = multiprocessing.cpu_count()
    if sys.platform == 'darwin': ncpu //= 2
    nenv = args.num_env or ncpu
    alg = args.alg
    seed = args.seed

    env_type, env_id = get_env_type(args.env)
    assert alg in registry, 'Unknown algorithm {}'.format(alg)

    if env_type in {'atari', 'retro'}:
        frame_stack_size = 4
    else:
        frame_stack_size = 1

    if registry[alg]['supports_vecenv']:
        env = make_vec_env(env_id, env_type, nenv, seed, gamestate=args.gamestate, reward_scale=args.reward_scale, frame_stack_size=frame_stack_size)
    else:
        env = env_thunk(env_id, env_type, seed=seed, wrapper_kwargs={'frame_stack': frame_stack_size > 1})

    if env_type == 'mujoco' and registry[alg]['supports_vecenv']:
        env = VecNormalize(env)

    return env


def get_env_type(env_id):
    if env_id in _game_envs.keys():
        env_type = env_id
        env_id = [g for g in _game_envs[env_type]][0]
    else:
        env_type = None
        for g, e in _game_envs.items():
            if env_id in e:
                env_type = g
                break
        assert env_type is not None, 'env_id {} is not recognized in env types'.format(env_id, _game_envs.keys())

    return env_type, env_id


def get_default_network(env_type):
    if env_type == 'atari':
        return 'cnn'
    else:
        return 'mlp'

def get_alg_module(alg, submodule=None):
    import inspect
    entry = registry.get(alg)
    assert entry is not None, 'Unregistered algorithm {}'.format(alg)
    module = inspect.getmodule(entry['fn']).__name__
    if submodule is not None:
        module = '.'.join([module, submodule])
    return module



def get_learn_function(alg):
    entry = registry.get(alg)
    assert entry is not None, 'Unregistered algorithm {}'.format(alg)
    return entry['fn']


def get_learn_function_defaults(alg, env_type):
    entry = registry.get(alg)
    assert entry is not None, 'Unregistered algorithm {}'.format(alg)
    return entry['defaults'].get(env_type, {})


def parse_cmdline_kwargs(args):
    '''
    convert a list of '='-spaced command-line arguments to a dictionary, evaluating python objects when possible
    '''
    def parse(v):

        assert isinstance(v, str)
        try:
            return eval(v)
        except (NameError, SyntaxError):
            return v

    return {k: parse(v) for k,v in parse_unknown_args(args).items()}



def main():
    # configure logger, disable logging in child MPI processes (with rank > 0)

    arg_parser = common_arg_parser()
    args, unknown_args = arg_parser.parse_known_args()
    extra_args = parse_cmdline_kwargs(unknown_args)

    if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
        rank = 0
        logger.configure()
    else:
        logger.configure(format_strs=[])
        rank = MPI.COMM_WORLD.Get_rank()

    model, env = train(args, extra_args)

    env.close()

    if args.save_path is not None and rank == 0:
        save_path = osp.expanduser(args.save_path)
        model.save(save_path)

    if args.play:
        logger.log("Running trained model")
        env = build_env(args)
        obs = env.reset()
        def initialize_placeholders(nlstm=128,**kwargs):
            return np.zeros((args.num_env or 1, 2*nlstm)), np.zeros((1))
        state, dones = initialize_placeholders(**extra_args)
        while True:
            actions, _, state, _ = model.step(obs,S=state, M=dones)
            obs, _, done, _ = env.step(actions)
            env.render()
            done = done.any() if isinstance(done, np.ndarray) else done

            if done:
                obs = env.reset()

        env.close()

if __name__ == '__main__':
    main()