sys.exit(
str(e) + '\n' +
'map_config import error. File not exist or map_config not specified'
)
from gym.envs.registration import register
register(
id='MonsterKong-v0',
entry_point='baselines.ple.gym_env.monsterkong:MonsterKongEnv',
kwargs={'map_config': map_config},
)
env = gym.make('MonsterKong-v0')
env = ProcessFrame(env)
else:
env = create_atari_environment(args.env, sticky_actions=False)
env = FrameStack(env, 4)
if args.seed > 0:
set_global_seeds(args.seed)
env.unwrapped.seed(args.seed)
print("obs shape", env.observation_space.shape)
# env = GIFRecorder(video_path=args.video_path + "/{}/".format(args.comment), record_video=True, env=env)
subdir = (datetime.datetime.now()
).strftime("%m-%d-%Y-%H:%M:%S") + " " + args.comment
tf_writer = tf.summary.FileWriter(os.path.join(args.log_dir, subdir),
tf.get_default_graph())
value_summary = tf.Summary()
qec_summary = tf.Summary()
value_summary.value.add(tag='discount_reward_mean')
value_summary.value.add(tag='non_discount_reward_mean')
# value_summary.value.add(tag='episode')
def create_env(args):
if args.env == "MK" or args.env == "mk":
import imp
try:
map_config_file = mk_map_config.get(args.env_name,
mk_map_config["small"])
map_config = imp.load_source('map_config',
map_config_file).map_config
except Exception as e:
sys.exit(
str(e) + '\n' +
'map_config import error. File not exist or map_config not specified'
)
from gym.envs.registration import register
register(
id='MonsterKong-v{}'.format(args.number),
entry_point='baselines.ple.gym_env.monsterkong:MonsterKongEnv',
kwargs={
'map_config': map_config,
'noise_size': args.noise_size
},
)
env = gym.make('MonsterKong-v{}'.format(args.number))
env = ProcessFrame(env)
env = MKPreprocessing(env, frame_skip=3, no_jump=True)
elif args.env == "GW" or args.env == "gw":
if args.env_name == "fourrooms":
env = ImageInputWarpper(Fourrooms())
elif args.env_name == "fourrooms_noise":
env = ImageNoisyEnv(ImageInputWarpper(Fourrooms()),
args.noise_size, 400)
elif args.env_name == "fourroomscoin":
env = ImageInputWarpper(FourroomsCoin())
else:
env = ImageNoisyEnv(ImageInputWarpper(FourroomsCoin()),
args.noise_size, 400)
elif args.env == "doom" or args.env == "Doom":
params = load_params(args.param_dir, args.env_name)
env = DoomEnvironment(**params['env_params'])
env = DoomPreprocessing(env, frame_skip=4)
elif args.env == "atari" or args.env == "Atari":
env = create_atari_environment(args.env_name, sticky_actions=False)
env = FrameStack(env, 4)
# if args.env_name == "Pong":
# env = CropWrapper(env, 34, 15)
elif args.env == "vast":
params = load_params("../atari/", args.env_name)
params["env_params"]['game'] = args.env_name
env = atari_env_vast(**params["env_params"])
elif args.env == "mujoco":
from gym.envs.registration import register
goal_args = [[0.0, 16.0], [0 + 1e-3, 16 + 1e-3], [0.0, 6.0],
[15 + 1e-3, 10 + 1e-3]]
random_start = False
# The episode length for test is 500
max_timestep = 500
register(
id='PointMazeTest-v10',
entry_point='mujoco.create_maze_env:create_maze_env',
kwargs={
'env_name': 'DiscretePointBlock',
'goal_args': goal_args,
'maze_size_scaling': 4,
'random_start': random_start,
'env_args': {
'amplify': args.amplify
}
},
max_episode_steps=max_timestep,
)
env = gym.make('PointMazeTest-v10')
elif args.env == "noise_atari" or args.env == "atari_noise":
game_version = 'v0'
env = gym.make('{}-{}'.format(args.env_name, game_version))
env = NoisyEnv(env, args.noise_dim, args.noise_var)
elif args.env == "noise_mujoco" or args.env == "mujoco_noise":
from gym.envs.registration import register
if args.env_name == "flip":
goal_args = [[0.0, 16.0], [0 + 1e-3, 16 + 1e-3], [0.0, 6.0],
[15 + 1e-3, 10 + 1e-3]]
else:
goal_args = [[16.0, 16.0], [16 + 1e-3, 16 + 1e-3], [1.0, 6.0],
[16 + 1e-3, 10 + 1e-3]]
random_start = False
# The episode length for test is 500
max_timestep = 500
try:
register(
id='PointMazeTest-v10',
entry_point='mujoco.create_maze_env:create_maze_env',
kwargs={
'env_name': 'DiscretePointBlock',
'goal_args': goal_args,
'maze_size_scaling': 4,
'random_start': random_start
},
max_episode_steps=max_timestep,
)
except gym.error.Error:
pass
env = gym.make('PointMazeTest-v10')
env = DynamicNoisyEnv(env,
args.noise_dim,
args.noise_var,
seed=args.seed)
# env = StaticNoisyEnv(env, args.noise_dim, args.noise_var, seed=args.seed)
elif args.env == "dreamer":
task = dreamer_tasks.cheetah_run({}, {'max_length': 500})
env = task[1]()
env = DiscreteWrapper(env)
elif args.env == "coinrun" or args.env == "CoinRun":
CoinrunConfig.initialize_args(False)
from coinrun.coinrun.coinrunenv import init_args_and_threads
init_args_and_threads(4)
# env_id = coinrun_game_versions.get(args.env_name,1000)
print("create env", args.env_name)
env = CoinRunVecEnv(args.env_name, 1)
# elif args.env == "d4rl":
# import d4rl
# env = gym.make(args.env_name)
#
# if "maze" in args.env_name:
# env = D4RLDiscreteMazeEnvWrapper(env)
else:
raise NotImplementedError
if args.seed > 0:
set_global_seeds(args.seed)
env.unwrapped.seed(args.seed)
return env
