Files
baselines/baselines/common/cmd_util.py
pzhokhov 353bb15e90 deduplicate algorithms in rl-algs and baselines (#18)
* move vec_env

* cleaning up rl_common

* tests are passing (but mosts tests are deleted as moved to baselines)

* add benchmark runner for smoke tests

* removed duplicated algos

* route references to rl_algs.a2c to baselines.a2c

* route references to rl_algs.a2c to baselines.a2c

* unify conftest.py

* removing references to duplicated algs from codegen

* removing references to duplicated algs from codegen

* alex's changes to dummy_vec_env

* fixed test_carpole[deepq] testcase by decreasing number of training steps... alex's changes seemed to have fixed the bug and make it train better, but at seed=0 there is a dip in the training curve at 30k steps that fails the test

* codegen tests with atol=1e-6 seem to be unstable

* rl_common.vec_env -> baselines.common.vec_env mass replace

* fixed reference in trpo_mpi

* a2c.util references

* restored rl_algs.bench in sonic_prob

* fix reference in ci/runtests.sh

* simplifed expression in baselines/common/cmd_util

* further increased rtol to 1e-3 in codegen tests

* switched vecenvs to use SimpleImageViewer from gym instead of cv2

* make run.py --play option work with num_envs > 1

* make rosenbrock test reproducible

* git subrepo pull (merge) baselines

subrepo:
  subdir:   "baselines"
  merged:   "e23524a5"
upstream:
  origin:   "git@github.com:openai/baselines.git"
  branch:   "master"
  commit:   "bcde04e7"
git-subrepo:
  version:  "0.4.0"
  origin:   "git@github.com:ingydotnet/git-subrepo.git"
  commit:   "74339e8"

* updated baselines README (num-timesteps --> num_timesteps)

* typo in deepq/README.md
2018-08-17 13:54:11 -07:00

128 lines
4.5 KiB
Python

"""
Helpers for scripts like run_atari.py.
"""
import os
try:
from mpi4py import MPI
except ImportError:
MPI = None
import gym
from gym.wrappers import FlattenDictWrapper
from baselines import logger
from baselines.bench import Monitor
from baselines.common import set_global_seeds
from baselines.common.atari_wrappers import make_atari, wrap_deepmind
from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
def make_atari_env(env_id, num_env, seed, wrapper_kwargs=None, start_index=0):
"""
Create a wrapped, monitored SubprocVecEnv for Atari.
"""
if wrapper_kwargs is None: wrapper_kwargs = {}
mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
def make_env(rank): # pylint: disable=C0111
def _thunk():
env = make_atari(env_id)
env.seed(seed + 10000*mpi_rank + rank if seed is not None else None)
env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(rank)))
return wrap_deepmind(env, **wrapper_kwargs)
return _thunk
set_global_seeds(seed)
return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])
def make_mujoco_env(env_id, seed, reward_scale=1.0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
rank = MPI.COMM_WORLD.Get_rank()
myseed = seed + 1000 * rank if seed is not None else None
set_global_seeds(myseed)
env = gym.make(env_id)
logger_path = None if logger.get_dir() is None else os.path.join(logger.get_dir(), str(rank))
env = Monitor(env, logger_path, allow_early_resets=True)
env.seed(seed)
if reward_scale != 1.0:
from baselines.common.retro_wrappers import RewardScaler
env = RewardScaler(env, reward_scale)
return env
def make_robotics_env(env_id, seed, rank=0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
env = FlattenDictWrapper(env, ['observation', 'desired_goal'])
env = Monitor(
env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),
info_keywords=('is_success',))
env.seed(seed)
return env
def arg_parser():
"""
Create an empty argparse.ArgumentParser.
"""
import argparse
return argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
def atari_arg_parser():
"""
Create an argparse.ArgumentParser for run_atari.py.
"""
print('Obsolete - use common_arg_parser instead')
return common_arg_parser()
def mujoco_arg_parser():
print('Obsolete - use common_arg_parser instead')
return common_arg_parser()
def common_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
parser.add_argument('--seed', help='RNG seed', type=int, default=None)
parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2')
parser.add_argument('--num_timesteps', type=float, default=1e6),
parser.add_argument('--network', help='network type (mlp, cnn, lstm, cnn_lstm, conv_only)', default=None)
parser.add_argument('--gamestate', help='game state to load (so far only used in retro games)', default=None)
parser.add_argument('--num_env', help='Number of environment copies being run in parallel. When not specified, set to number of cpus for Atari, and to 1 for Mujoco', default=None, type=int)
parser.add_argument('--reward_scale', help='Reward scale factor. Default: 1.0', default=1.0, type=float)
parser.add_argument('--save_path', help='Path to save trained model to', default=None, type=str)
parser.add_argument('--play', default=False, action='store_true')
return parser
def robotics_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='FetchReach-v0')
parser.add_argument('--seed', help='RNG seed', type=int, default=None)
parser.add_argument('--num-timesteps', type=int, default=int(1e6))
return parser
def parse_unknown_args(args):
"""
Parse arguments not consumed by arg parser into a dicitonary
"""
retval = {}
for arg in args:
assert arg.startswith('--')
assert '=' in arg, 'cannot parse arg {}'.format(arg)
key = arg.split('=')[0][2:]
value = arg.split('=')[1]
retval[key] = value
return retval