def _thunk():
if env_id.startswith("dm"):
_, domain, task = env_id.split('.')
env = dmc2gym.make(domain_name=domain, task_name=task)
env = ClipAction(env)
elif env_id.startswith("rrc"):
_, ac_type, ac_wrapper = env_id.split('.')
ts_relative, sa_relative = False, False
scaled_ac, task_space = False, False
if ac_wrapper.split('-')[0] == 'task':
task_space = True
ts_relative = ac_wrapper.split('-')[-1] == 'rel'
elif ac_wrapper.split('-')[0] == 'scaled':
scaled_ac = True
sa_relative = ac_wrapper.split('-')[-1] == 'rel'
env = rrc_utils.build_env_fn(
action_type=ac_type, initializer=None, scaled_ac=scaled_ac,
task_space=task_space, sa_relative=sa_relative,
ts_relative=ts_relative, goal_relative=True,
rew_fn='step')()
else:
env = gym.make(env_id)
is_atari = hasattr(gym.envs, 'atari') and isinstance(
env.unwrapped, gym.envs.atari.atari_env.AtariEnv)
if is_atari:
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env.seed(seed + rank)
if str(env.__class__.__name__).find('TimeLimit') >= 0:
env = TimeLimitMask(env)
if log_dir is not None:
env = Monitor(env,
os.path.join(log_dir, str(rank)),
allow_early_resets=allow_early_resets)
if is_atari:
if len(env.observation_space.shape) == 3:
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env, width=84, height=84)
env = ClipRewardEnv(env)
elif len(env.observation_space.shape) == 3:
raise NotImplementedError(
"CNN models work only for atari,\n"
"please use a custom wrapper for a custom pixel input env.\n"
"See wrap_deepmind for an example.")
# If the input has shape (W,H,3), wrap for PyTorch convolutions
obs_shape = env.observation_space.shape
if len(obs_shape) == 3 and obs_shape[2] in [1, 3]:
env = TransposeImage(env, op=[2, 0, 1])
return env
def _thunk():
print(f"Using {env_id} environment")
if env_id == "Warehouse":
env = Warehouse(parameters)
elif env_id == 'Sumo':
# todo currently just using loop_network scene
params = {'scene': "loop_network", 'libsumo': True}
env = LoopNetwork(seed, params)
else:
if env_id.startswith("dm"):
_, domain, task = env_id.split('.')
env = dmc2gym.make(domain_name=domain, task_name=task)
env = ClipAction(env)
else:
env = gym.make(env_id)
is_atari = hasattr(gym.envs, 'atari') and isinstance(
env.unwrapped, gym.envs.atari.atari_env.AtariEnv)
if is_atari:
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env.seed(seed + rank)
if str(env.__class__.__name__).find('TimeLimit') >= 0:
env = TimeLimitMask(env)
if env_id not in ["Warehouse", "Sumo"]:
if log_dir is not None:
env = Monitor(env,
os.path.join(log_dir, str(rank)),
allow_early_resets=allow_early_resets)
if is_atari:
if len(env.observation_space.shape) == 3:
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env, width=84, height=84)
env = ClipRewardEnv(env)
elif len(env.observation_space.shape) == 3:
raise NotImplementedError(
"CNN models work only for atari,\n"
"please use a custom wrapper for a custom pixel input env.\n"
"See wrap_deepmind for an example.")
# If the input has shape (W,H,3), wrap for PyTorch convolutions
obs_shape = env.observation_space.shape
if len(obs_shape) == 3 and obs_shape[2] in [1, 3]:
env = TransposeImage(env, op=[2, 0, 1])
return env
def _thunk():
if env_id.startswith("dm"):
_, domain, task = env_id.split('.')
env = dm_control2gym.make(domain_name=domain, task_name=task)
else:
env = gym.make(env_id)
is_atari = hasattr(gym.envs, 'atari') and isinstance(
env.unwrapped, gym.envs.atari.atari_env.AtariEnv)
if is_atari:
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env.seed(seed + rank)
if str(env.__class__.__name__).find('TimeLimit') >= 0:
env = TimeLimitMask(env)
if log_dir is not None:
env = Monitor(
env,
os.path.join(log_dir, str(rank)),
allow_early_resets=allow_early_resets)
if is_atari:
if len(env.observation_space.shape) == 3:
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env, width=84, height=84)
env = ClipRewardEnv(env)
elif len(env.observation_space.shape) == 3:
# env = EpisodicLifeEnv(env)
# if 'FIRE' in env.unwrapped.get_action_meanings():
# env = FireResetEnv(env)
env = WarpFrame(env, width=64, height=64)
# raise NotImplementedError(
# "CNN models work only for atari,\n"
# "please use a custom wrapper for a custom pixel input env.\n"
# "See wrap_deepmind for an example.")
# If the input has shape (W,H,3), wrap for PyTorch convolutions
obs_shape = env.observation_space.shape
if len(obs_shape) == 3 and obs_shape[2] in [1, 3]:
env = TransposeImage(env, op=[2, 0, 1])
return env
def thunk():
env = gym.make(gym_id)
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env = gym.wrappers.RecordEpisodeStatistics(env)
if args.capture_video:
if idx == 0:
env = Monitor(env, f"videos/{experiment_name}")
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env, width=84, height=84)
env = ClipRewardEnv(env)
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
def _thunk():
if env_id == "dummy_env":
env = DummyEnv()
elif env_id == "dummy_multi_head_env":
env = DummyMultiHeadEnv()
elif env_id == "platform":
env = PlatformWrapper()
else:
env = gym.make(env_id)
is_atari = hasattr(gym.envs, 'atari') and isinstance(
env.unwrapped, gym.envs.atari.atari_env.AtariEnv)
if is_atari:
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env.seed(seed + rank)
if str(env.__class__.__name__).find('TimeLimit') >= 0:
env = TimeLimitMask(env)
if log_dir is not None:
env = Monitor(env,
os.path.join(log_dir, str(rank)),
allow_early_resets=allow_early_resets)
if is_atari:
if len(env.observation_space.shape) == 3:
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env, width=84, height=84)
env = ClipRewardEnv(env)
elif len(env.observation_space.shape) == 3:
raise NotImplementedError(
"CNN models work only for atari,\n"
"please use a custom wrapper for a custom pixel input env.\n"
"See wrap_deepmind for an example.")
# If the input has shape (W,H,3), wrap for PyTorch convolutions
obs_shape = env.observation_space.shape
if len(obs_shape) == 3 and obs_shape[2] in [1, 3]:
env = TransposeImage(env, op=[2, 0, 1])
return env
def __init__(
self,
env: gym.Env,
frame_skip: int = 4,
screen_size: int = 84,
terminal_on_life_loss: bool = True,
clip_reward: bool = True,
):
env = MaxAndSkipEnv(env, skip=frame_skip)
if terminal_on_life_loss:
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env, width=screen_size, height=screen_size)
if clip_reward:
env = ClipRewardEnv(env)
super(AtariWrapper, self).__init__(env)
def thunk():
env = gym.make(gym_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
if capture_video:
if idx == 0:
env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.ResizeObservation(env, (84, 84))
env = gym.wrappers.GrayScaleObservation(env)
env = gym.wrappers.FrameStack(env, 4)
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
def __init__(
self,
env: gym.Env,
noop_max: int = 30,
frame_skip: int = 4,
screen_sizeH: int = 116,
screen_sizeW: int = 124,
terminal_on_life_loss: bool = True,
clip_reward: bool = True,
):
env = NoopResetEnv(env, noop_max=noop_max)
env = MaxAndSkipEnv(env, skip=frame_skip)
if terminal_on_life_loss:
env = SMEpisodicLifeEnv(env)
env = WarpFrame(env, width=screen_sizeW, height=screen_sizeH)
env = RewardWraper(env)
if clip_reward:
env = ClipRewardEnv(env)
super().__init__(env)