class_docstr: Defines a configuration class from which an AlphaStar Algorithm can be built.
  
  Example:
      >>> from ray.rllib.algorithms.alpha_star import AlphaStarConfig
      >>> config = AlphaStarConfig().training(lr=0.0003, train_batch_size=512)        ...     .resources(num_gpus=4)        ...     .rollouts(num_rollout_workers=64)
      >>> print(config.to_dict()) # doctest: +SKIP
      >>> # Build a Algorithm object from the config and run 1 training iteration.
      >>> algo = config.build(env="CartPole-v1")
      >>> algo.train() # doctest: +SKIP
  
  Example:
      >>> from ray.rllib.algorithms.alpha_star import AlphaStarConfig
      >>> from ray import air
      >>> from ray import tune
      >>> config = AlphaStarConfig()
      >>> # Print out some default values.
      >>> print(config.vtrace) # doctest: +SKIP
      >>> # Update the config object.
      >>> config.training(lr=tune.grid_search([0.0001, 0.0003]), grad_clip=20.0)
      >>> # Set the config object's env.
      >>> config.environment(env="CartPole-v1")
      >>> # Use to_dict() to get the old-style python config dict
      >>> # when running with tune.
      >>> tune.Tuner( # doctest: +SKIP
      ...     "AlphaStar",
      ...     run_config=air.RunConfig(stop={"episode_reward_mean": 200}),
      ...     param_space=config.to_dict(),
      ... ).fit()
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docstr: Initializes a AlphaStarConfig instance.
docstr_tok: ["Initializes","a","AlphaStarConfig","instance","."]
imports: from typing import Any, Dict, Optional, Type, Union
  import gymnasium
  import tree
  from rllib_alpha_star.alpha_star.distributed_learners import DistributedLearners
  from rllib_alpha_star.alpha_star.league_builder import AlphaStarLeagueBuilder
  import ray
  import ray.rllib.algorithms.appo.appo
  from ray.actor import ActorHandle
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.execution.buffers.mixin_replay_buffer import MixInMultiAgentReplayBuffer
  from ray.rllib.execution.parallel_requests import AsyncRequestsManager
  from ray.rllib.policy.policy import Policy, PolicySpec
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils import deep_update
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.metrics import LAST_TARGET_UPDATE_TS, LEARN_ON_BATCH_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_TARGET_UPDATES, SAMPLE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER, TARGET_NET_UPDATE_TIMER
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.typing import PartialAlgorithmConfigDict, PolicyID, PolicyState, ResultDict
  from ray.tune.execution.placement_groups import PlacementGroupFactory
  from ray.util.timer import _Timer
label_desc: Use image node_id 1 to create a new AlphaStarConfig object from inherited base classes: appo with example: obj = AlphaStarConfig(algo_class)