src.environment.parallelenv.worker.ray

Module Contents

Classes

_SetAttrWrapper

RayEnvWorker

Ray worker used in RayVectorEnv.

API

class src.environment.parallelenv.worker.ray._SetAttrWrapper[source]

Bases: gym.Wrapper

set_env_attr(key: str, value: Any) → None[source]
get_env_attr(key: str) → Any[source]
class src.environment.parallelenv.worker.ray.RayEnvWorker(env_fn: List[Callable[[], gym.Env]], num_cpu_per_worker: int = 1, num_gpu_per_worker: int = 0, no_warning=False)[source]

Bases: src.environment.parallelenv.worker.base.EnvWorker

Ray worker used in RayVectorEnv.

Initialization

num_cpu_per_worker[source]

1

num_gpu_per_worker[source]

0

get_env_attr(key: str) → Any[source]
set_env_attr(key: str, value: Any) → None[source]
get_env_obj()[source]
send_reset() → Any[source]
ray_reset(no_warning)[source]
static wait(workers: List[src.environment.parallelenv.worker.ray.RayEnvWorker], wait_num: int, timeout: Optional[float] = None) → List[src.environment.parallelenv.worker.ray.RayEnvWorker][source]
send(action: Optional[numpy.ndarray]) → None[source]
ray_step(action, no_warning)[source]
customized_method(func: str, data=None) → Any[source]
ray_customized(func, data=None, no_warning=False)[source]
ray_rollout(no_warning=False)[source]
rollout()[source]
recv() → Union[Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], numpy.ndarray][source]
recv_once() → Union[Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], numpy.ndarray][source]
seed(seed: Optional[int] = None) → List[int][source]
render(**kwargs: Any) → Any[source]
close_env() → None[source]