def get_model_info(name: str) -> Tuple[Callable, Dict, str, str]:
'''
Args:
name: name of algorithms
Return:
algo_class of the algorithm model named `name`.
defaulf config of specified algorithm.
policy_type of policy, `on-policy` or `off-policy`
'''
if name not in algos.keys():
raise NotImplementedError(name)
else:
class_name = algos[name]['algo_class']
policy_mode = algos[name]['policy_mode']
policy_type = algos[name]['policy_type']
model = getattr(
importlib.import_module(f'rls.algos.{policy_type}.{name}'),
class_name)
algo_config = load_yaml(f'rls/algos/config.yaml')[name]
algo_config.update(
load_yaml(f'rls/algos/config.yaml')[policy_mode.replace('-', '_')])
algo_config.update(load_yaml(f'rls/algos/config.yaml')['general'])
return model, algo_config, policy_mode, policy_type
def get_model_info(name: str) -> Tuple[Callable, Dict, str, str]:
'''
Args:
name: name of algorithms
Return:
algo_class of the algorithm model named `name`.
defaulf config of specified algorithm.
policy_type of policy, `on-policy` or `off-policy`
'''
algo_info = registry.get_model_info(name)
class_name = algo_info['algo_class']
policy_mode = algo_info['policy_mode']
policy_type = algo_info['policy_type']
LOGO = algo_info.get('logo', '')
logger.info(colorize(LOGO, color='green'))
model = getattr(
importlib.import_module(f'rls.algos.{policy_type}.{name}'),
class_name)
algo_config = {}
algo_config.update(
load_yaml(f'rls/algos/config.yaml')['general']
)
algo_config.update(
load_yaml(f'rls/algos/config.yaml')[policy_mode.replace('-', '_')]
)
algo_config.update(
load_yaml(f'rls/algos/config.yaml')[name]
)
return model, algo_config, policy_mode, policy_type
def build_env(config: Dict):
gym_env_name = config['env_name']
action_skip = bool(config.get('action_skip', False))
skip = int(config.get('skip', 4))
obs_stack = bool(config.get('obs_stack', False))
stack = int(config.get('stack', 4))
noop = bool(config.get('noop', False))
noop_max = int(config.get('noop_max', 30))
obs_grayscale = bool(config.get('obs_grayscale', False))
obs_resize = bool(config.get('obs_resize', False))
resize = config.get('resize', [84, 84])
obs_scale = bool(config.get('obs_scale', False))
max_episode_steps = config.get('max_episode_steps', None)
env_type = get_env_type(gym_env_name)
env = gym.make(gym_env_name)
env = BaseEnv(env)
if env_type == 'atari':
assert 'NoFrameskip' in env.spec.id
from rls.common.yaml_ops import load_yaml
default_config = load_yaml(
f'{os.path.dirname(__file__)}/config.yaml')['atari']
env = make_atari(env, default_config)
else:
if gym_env_name.split('-')[0] == 'MiniGrid':
env = gym_minigrid.wrappers.RGBImgPartialObsWrapper(
env) # Get pixel observations, or RGBImgObsWrapper
env = gym_minigrid.wrappers.ImgObsWrapper(
env) # Get rid of the 'mission' field
if noop and isinstance(env.observation_space, Box) and len(
env.observation_space.shape) == 3:
env = NoopResetEnv(env, noop_max=noop_max)
if action_skip:
env = MaxAndSkipEnv(env, skip=4)
if isinstance(env.observation_space, Box):
if len(env.observation_space.shape) == 3:
if obs_grayscale or obs_resize:
env = GrayResizeEnv(env,
resize=obs_resize,
grayscale=obs_grayscale,
width=resize[0],
height=resize[-1])
if obs_scale:
env = ScaleEnv(env)
if obs_stack:
env = StackEnv(env, stack=stack)
else:
env = OneHotObsEnv(env)
env = TimeLimit(env, max_episode_steps)
if isinstance(env.action_space, Box) and len(env.action_space.shape) == 1:
env = BoxActEnv(env)
if not (isinstance(env.observation_space, Box)
and len(env.observation_space.shape) == 3):
env = DtypeEnv(env)
return env
def main():
options = docopt(__doc__)
options = get_options(dict(options))
show_dict(options.to_dict)
trails = options.models
assert trails > 0, '--models must greater than 0.'
env_args, buffer_args, train_args = parse_options(
options, default_config=load_yaml(f'./config.yaml'))
if options.inference:
Trainer(env_args, buffer_args, train_args).evaluate()
return
if options.apex is not None:
train_args.update(load_yaml(f'./rls/distribute/apex/config.yaml'))
Trainer(env_args, buffer_args, train_args).apex()
else:
if trails == 1:
agent_run(env_args, buffer_args, train_args)
elif trails > 1:
processes = []
for i in range(trails):
_env_args, _buffer_args, _train_args = map(
deepcopy, [env_args, buffer_args, train_args])
_train_args.seed += i * 10
_train_args.name += f'/{i}'
_train_args.allow_print = True # NOTE: set this could block other processes' print function
if _env_args.type == 'unity':
_env_args.port = env_args.port + i
p = Process(target=agent_run,
args=(_env_args, _buffer_args, _train_args))
p.start()
time.sleep(10)
processes.append(p)
[p.join() for p in processes]
def __init__(self, env_args):
self.engine_configuration_channel = EngineConfigurationChannel()
if env_args['train_mode']:
self.engine_configuration_channel.set_configuration_parameters(
time_scale=env_args['train_time_scale'])
else:
self.engine_configuration_channel.set_configuration_parameters(
width=env_args['width'],
height=env_args['height'],
quality_level=env_args['quality_level'],
time_scale=env_args['inference_time_scale'],
target_frame_rate=env_args['target_frame_rate'])
self.float_properties_channel = EnvironmentParametersChannel()
if env_args['file_path'] is None:
self._env = UnityEnvironment(base_port=5004,
seed=env_args['env_seed'],
side_channels=[
self.engine_configuration_channel,
self.float_properties_channel
])
else:
unity_env_dict = load_yaml('/'.join(
[os.getcwd(), 'rls', 'envs', 'unity_env_dict.yaml']))
self._env = UnityEnvironment(
file_name=env_args['file_path'],
base_port=env_args['port'],
no_graphics=not env_args['render'],
seed=env_args['env_seed'],
side_channels=[
self.engine_configuration_channel,
self.float_properties_channel
],
additional_args=[
'--scene',
str(
unity_env_dict.get(env_args.get('env_name', 'Roller'),
'None')), '--n_agents',
str(env_args.get('env_num', 1))
])
self.reset_config = env_args['reset_config']
def __init__(self, kwargs):
self._side_channels = self.initialize_all_side_channels(kwargs)
env_kwargs = dict(seed=int(kwargs['env_seed']),
worker_id=int(kwargs['worker_id']),
timeout_wait=int(kwargs['timeout_wait']),
side_channels=list(
self._side_channels.values())) # 注册所有初始化后的通讯频道
if kwargs['file_name'] is not None:
unity_env_dict = load_yaml('/'.join(
[os.getcwd(), 'rls', 'envs', 'unity_env_dict.yaml']))
env_kwargs.update(file_name=kwargs['file_name'],
base_port=kwargs['port'],
no_graphics=not kwargs['render'],
additional_args=[
'--scene',
str(
unity_env_dict.get(
kwargs.get('env_name', '3DBall'),
'None'))
])
self.env = UnityEnvironment(**env_kwargs)
self.env.reset()
self.initialize_environment()
def build_env(config: Dict, index: int = 0):
gym_env_name = config['env_name']
env_type = get_env_type(gym_env_name)
env_params = {}
if env_type == 'pybullet_envs.bullet':
env_params.update({'renders': bool(config.get('inference', False))})
elif env_type == 'gym_donkeycar.envs.donkey_env':
_donkey_conf = load_yaml(
f'{os.path.dirname(__file__)}/config.yaml')['donkey']
import uuid
# [120, 160, 3]
_donkey_conf['port'] += index
_donkey_conf['car_name'] += str(index)
_donkey_conf['guid'] = str(uuid.uuid4())
env_params['conf'] = _donkey_conf
env = gym.make(gym_env_name, **env_params)
env = BaseEnv(env)
if env_type == 'gym.envs.atari':
assert 'NoFrameskip' in env.spec.id, 'env id should contain NoFrameskip.'
default_config = load_yaml(
f'{os.path.dirname(__file__)}/config.yaml')['atari']
env = make_atari(env, default_config)
else:
action_skip = bool(config.get('action_skip', False))
skip = int(config.get('skip', 4))
obs_stack = bool(config.get('obs_stack', False))
stack = int(config.get('stack', 4))
noop = bool(config.get('noop', False))
noop_max = int(config.get('noop_max', 30))
obs_grayscale = bool(config.get('obs_grayscale', False))
obs_resize = bool(config.get('obs_resize', False))
resize = config.get('resize', [84, 84])
obs_scale = bool(config.get('obs_scale', False))
max_episode_steps = config.get('max_episode_steps', None)
if gym_env_name.split('-')[0] == 'MiniGrid':
env = gym_minigrid.wrappers.RGBImgPartialObsWrapper(
env) # Get pixel observations, or RGBImgObsWrapper
env = gym_minigrid.wrappers.ImgObsWrapper(
env) # Get rid of the 'mission' field
if noop and isinstance(env.observation_space, Box) and len(
env.observation_space.shape) == 3:
env = NoopResetEnv(env, noop_max=noop_max)
if action_skip:
env = MaxAndSkipEnv(env, skip=4)
if isinstance(env.observation_space, Box):
if len(env.observation_space.shape) == 3:
if obs_grayscale or obs_resize:
env = GrayResizeEnv(env,
resize=obs_resize,
grayscale=obs_grayscale,
width=resize[0],
height=resize[-1])
if obs_scale:
env = ScaleEnv(env)
if obs_stack:
env = StackEnv(env, stack=stack)
else:
env = OneHotObsEnv(env)
env = TimeLimit(env, max_episode_steps)
if isinstance(env.action_space, Box) and len(env.action_space.shape) == 1:
env = BoxActEnv(env)
if not (isinstance(env.observation_space, Box)
and len(env.observation_space.shape) == 3):
env = DtypeEnv(env)
return env
