code: def __init__(
      self,
      observation_space,
      action_space,
      config,
      model,
      loss,
      action_distribution_class,
      mcts_creator,
      env_creator,
      **kwargs,
  ):
      super().__init__(
          observation_space,
          action_space,
          config,
          model=model,
          loss=loss,
          action_distribution_class=action_distribution_class,
      )
      # we maintain an env copy in the policy that is used during mcts
      # simulations
      self.env_creator = env_creator
      self.mcts = mcts_creator()
      self.env = self.env_creator()
      self.env.reset()
      self.obs_space = observation_space
  
file_path: ray/rllib_contrib/alpha_zero/src/rllib_alpha_zero/alpha_zero/alpha_zero_policy.py
imports: import numpy
  from rllib_alpha_zero.alpha_zero.mcts import Node, RootParentNode
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.torch_policy import TorchPolicy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
label_desc: Use image node_id 1 to create a new AlphaZeroPolicy object from inherited base classes: TorchPolicy with example: obj = AlphaZeroPolicy(observation_space, action_space, config, model, loss, action_distribution_class, mcts_creator, env_creator)