ray/rllib/agents/impala/__init__.py

from ray.rllib.algorithms.impala.impala import (
    DEFAULT_CONFIG,
    ImpalaConfig,
    Impala as ImpalaTrainer,
)
from ray.rllib.utils.deprecation import deprecation_warning


__all__ = [
    "ImpalaConfig",
    "ImpalaTrainer",
    "DEFAULT_CONFIG",
]

deprecation_warning(
    "ray.rllib.agents.impala", "ray.rllib.algorithms.impala", error=False
)
[RLlib] Move (A/DD)?PPO and IMPALA algos to `algorithms` dir and rename policy and trainer classes. (#25346) 2022-06-02 16:47:05 +02:00			`from ray.rllib.algorithms.impala.impala import (`
			`DEFAULT_CONFIG,`
			`ImpalaConfig,`
			`Impala as ImpalaTrainer,`
			`)`
			`from ray.rllib.utils.deprecation import deprecation_warning`

[rllib] Basic IMPALA implementation (using deepmind's reference vtrace.py) (#2504) Rename AsyncSamplesOptimizer -> AsyncReplayOptimizer Add AsyncSamplesOptimizer that implements the IMPALA architecture integrate V-trace with a3c policy graph audit V-trace integration benchmark compare vs A3C and with V-trace on/off PongNoFrameskip-v4 on IMPALA scaling from 16 to 128 workers, solving Pong in <10 min. For reference, solving this env takes ~40 minutes for Ape-X and several hours for A3C. 2018-08-01 20:53:53 -07:00
[RLlib] Trainer sub-class IMPALA (instead of using `build_trainer()`). (#20570) 2021-11-30 19:08:36 +01:00			`__all__ = [`
[RLlib] IMPALA TrainerConfig objects. (#24375) 2022-05-02 12:05:30 +02:00			`"ImpalaConfig",`
[RLlib] Trainer sub-class IMPALA (instead of using `build_trainer()`). (#20570) 2021-11-30 19:08:36 +01:00			`"ImpalaTrainer",`
[RLlib] IMPALA TrainerConfig objects. (#24375) 2022-05-02 12:05:30 +02:00			`"DEFAULT_CONFIG",`
[RLlib] Trainer sub-class IMPALA (instead of using `build_trainer()`). (#20570) 2021-11-30 19:08:36 +01:00			`]`
[RLlib] Move (A/DD)?PPO and IMPALA algos to `algorithms` dir and rename policy and trainer classes. (#25346) 2022-06-02 16:47:05 +02:00
			`deprecation_warning(`
			`"ray.rllib.agents.impala", "ray.rllib.algorithms.impala", error=False`
			`)`