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run.py
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run.py
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import sys
import re
import multiprocessing
import os.path as osp
import gym
from collections import defaultdict
import tensorflow as tf
import numpy as np
from baselines.common.vec_env import VecFrameStack, VecNormalize, VecEnv
from baselines.common.vec_env.vec_video_recorder import VecVideoRecorder
from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, make_env
from baselines import logger
from importlib import import_module
try:
from mpi4py import MPI
except ImportError:
MPI = None
# Fix for loading Roboschool environments
try:
from OpenGL import GLU
except ImportError:
pass
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)
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.save_video_interval != 0:
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)
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)
if env_type in {'atari', 'retro'}:
if alg == 'deepq':
env = make_env(env_id, env_type, seed=seed, wrapper_kwargs={'frame_stack': True})
elif alg == 'trpo_mpi':
env = make_env(env_id, env_type, seed=seed)
else:
frame_stack_size = 4
env = make_vec_env(env_id, env_type, nenv, seed, gamestate=args.gamestate, reward_scale=args.reward_scale)
env = VecFrameStack(env, frame_stack_size)
else:
flatten_dict_observations = alg not in {'her'}
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)
if env_type == 'mujoco':
env = VecNormalize(env)
return env
def get_env_type(args):
env_id = args.env
if args.env_type is not None:
return args.env_type, env_id
# Re-parse the gym registry, since we could have new envs since last time.
for env in gym.envs.registry.all():
env_type = env._entry_point.split(':')[0].split('.')[-1]
_game_envs[env_type].add(env.id) # This is a set so add is idempotent
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
if ':' in env_id:
env_type = re.sub(r':.*', '', env_id)
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 in {'atari', 'retro'}:
return 'cnn'
else:
return 'mlp'
def get_alg_module(alg, submodule=None):
submodule = submodule or alg
try:
# first try to import the alg module from baselines
alg_module = import_module('.'.join(['baselines', alg, submodule]))
except ImportError:
# then from rl_algs
alg_module = import_module('.'.join(['rl_' + 'algs', alg, submodule]))
return alg_module
def get_learn_function(alg):
return get_alg_module(alg).learn
def get_learn_function_defaults(alg, env_type):
try:
alg_defaults = get_alg_module(alg, 'defaults')
kwargs = getattr(alg_defaults, env_type)()
except (ImportError, AttributeError):
kwargs = {}
return kwargs
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 configure_logger(log_path, **kwargs):
if log_path is not None:
logger.configure(log_path)
else:
logger.configure(**kwargs)
def main(args):
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args(args)
extra_args = parse_cmdline_kwargs(unknown_args)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
configure_logger(args.log_path)
else:
rank = MPI.COMM_WORLD.Get_rank()
configure_logger(args.log_path, format_strs=[])
model, env = train(args, extra_args)
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
ckpt = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(ckpt, save_path, max_to_keep=None)
manager.save()
if args.play:
logger.log("Running trained model")
obs = env.reset()
if not isinstance(env, VecEnv):
obs = np.expand_dims(np.array(obs), axis=0)
state = model.initial_state if hasattr(model, 'initial_state') else None
dones = np.zeros((1,))
episode_rew = 0
while True:
if state is not None:
actions, _, state, _ = model.step(obs)
else:
actions, _, _, _ = model.step(obs)
obs, rew, done, _ = env.step(actions.numpy())
if not isinstance(env, VecEnv):
obs = np.expand_dims(np.array(obs), axis=0)
episode_rew += rew[0] if isinstance(env, VecEnv) else rew
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
print('episode_rew={}'.format(episode_rew))
episode_rew = 0
obs = env.reset()
env.close()
return model
if __name__ == '__main__':
main(sys.argv)