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run_atari.py
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run_atari.py
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#!/usr/bin/env python3
import functools
import os
import datetime
from baselines import logger
from mpi4py import MPI
import mpi_util
import tf_util
from cmd_util import make_atari_env, arg_parser
from policies.cnn_gru_policy_dynamics import CnnGruPolicy
from policies.cnn_policy_param_matched import CnnPolicy
from ppo_agent import PpoAgent
from utils import set_global_seeds
from vec_env import VecFrameStack
def train(*, env_id, num_env, hps, num_timesteps, seed):
venv = VecFrameStack(
make_atari_env(env_id, num_env, seed, wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
hps.pop('frame_stack'))
# venv.score_multiple = {'Mario': 500,
# 'MontezumaRevengeNoFrameskip-v4': 100,
# 'GravitarNoFrameskip-v4': 250,
# 'PrivateEyeNoFrameskip-v4': 500,
# 'SolarisNoFrameskip-v4': None,
# 'VentureNoFrameskip-v4': 200,
# 'PitfallNoFrameskip-v4': 100,
# }[env_id]
venv.score_multiple = 1
venv.record_obs = True if env_id == 'SolarisNoFrameskip-v4' else False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy,
'cnn': CnnPolicy}[hps.pop('policy')]
action_balance_coef = hps.pop('action_balance_coef')
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop('update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop('proportion_of_exp_used_for_predictor_update'),
dynamics_bonus=hps.pop("dynamics_bonus"),
action_balance_coef=action_balance_coef,
array_action=hps.pop('array_action')
),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
action_balance_coef=action_balance_coef
)
agent.start_interaction([venv])
if hps.pop('update_ob_stats_from_random_agent'):
agent.collect_random_statistics(num_timesteps=128 * 50)
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
counter = 0
while True:
info = agent.step()
if info['update']:
info['update'].pop('rooms')
logger.logkvs(info['update'])
logger.dumpkvs()
counter += 1
if agent.I.stats['tcount'] > num_timesteps:
break
checkdir = os.path.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = os.path.join(checkdir, '%.5i' % agent.I.stats['n_updates'])
tf_util.save_variables(save_path=savepath)
print('Saving to', savepath)
agent.stop_interaction()
def add_env_params(parser):
parser.add_argument('--env', help='environment ID', default='MontezumaRevengeNoFrameskip-v4')
parser.add_argument('--seed', help='RNG seed', type=int, default=0)
parser.add_argument('--max_episode_steps', type=int, default=4500)
def main():
default_log_dir = "/tmp/rnd_log"
parser = arg_parser()
add_env_params(parser)
parser.add_argument('--num-timesteps', type=int, default=int(4.2e7)) # 10k
parser.add_argument('--num_env', type=int, default=32)
parser.add_argument('--use_news', type=int, default=0)
parser.add_argument('--gamma', type=float, default=0.99)
# parser.add_argument('--gamma_ext', type=float, default=0.99)
parser.add_argument('--gamma_ext', type=float, default=0.999)
parser.add_argument('--lam', type=float, default=0.95)
parser.add_argument('--update_ob_stats_every_step', type=int, default=0)
parser.add_argument('--update_ob_stats_independently_per_gpu', type=int, default=0)
parser.add_argument('--update_ob_stats_from_random_agent', type=int, default=1)
# parser.add_argument('--proportion_of_exp_used_for_predictor_update', type=float, default=1.)
parser.add_argument('--proportion_of_exp_used_for_predictor_update', type=float, default=0.25)
parser.add_argument('--tag', type=str, default='')
parser.add_argument('--policy', type=str, default='cnn', choices=['cnn', 'rnn'])
parser.add_argument('--int_coeff', type=float, default=1.)
parser.add_argument('--ext_coeff', type=float, default=2.)
parser.add_argument('--dynamics_bonus', type=int, default=0)
parser.add_argument('--logdir', type=str, default=default_log_dir)
parser.add_argument('--action_balance_coef', '--abc', type=float, default=None)
parser.add_argument('--array_action', type=int, default=1)
parser.add_argument('--num_minibatches', type=int, default=4)
args = parser.parse_args()
if args.logdir != default_log_dir and os.path.isdir(args.logdir) and os.listdir(args.logdir):
raise ValueError("logdir not empty!")
logger.configure(dir=args.logdir,
format_strs=['stdout', 'log', 'csv', 'tensorboard'] if MPI.COMM_WORLD.Get_rank() == 0 else [])
if MPI.COMM_WORLD.Get_rank() == 0:
with open(os.path.join(logger.get_dir(), 'experiment_tag.txt'), 'w') as f:
f.write(args.tag)
# shutil.copytree(os.path.dirname(os.path.abspath(__file__)), os.path.join(logger.get_dir(), 'code'))
mpi_util.setup_mpi_gpus()
seed = 10000 * args.seed + MPI.COMM_WORLD.Get_rank()
set_global_seeds(seed)
hps = dict(
frame_stack=4,
nminibatches=args.num_minibatches,
nepochs=4,
lr=0.0001,
max_grad_norm=0.0,
use_news=args.use_news,
gamma=args.gamma,
gamma_ext=args.gamma_ext,
max_episode_steps=args.max_episode_steps,
lam=args.lam,
update_ob_stats_every_step=args.update_ob_stats_every_step,
update_ob_stats_independently_per_gpu=args.update_ob_stats_independently_per_gpu,
update_ob_stats_from_random_agent=args.update_ob_stats_from_random_agent,
proportion_of_exp_used_for_predictor_update=args.proportion_of_exp_used_for_predictor_update,
policy=args.policy,
int_coeff=args.int_coeff,
ext_coeff=args.ext_coeff,
dynamics_bonus=args.dynamics_bonus,
action_balance_coef=args.action_balance_coef,
array_action=args.array_action
)
logger.info('args: {}'.format(args))
tf_util.make_session(make_default=True)
train(env_id=args.env, num_env=args.num_env, seed=seed,
num_timesteps=args.num_timesteps, hps=hps)
if __name__ == '__main__':
main()