class_docstr: Evaluates a given policy by rolling it out and collecting the mean reward.
  
  :param eval_env: Distributed environment to run evaluation rollouts in.
  :param n_episodes: Number of evaluation episodes to run. Note that the actual number might be slightly larger
                      due to the distributed nature of the environment.
  :param model_selection: Model selection to notify about the recorded rewards.
  :param deterministic: deterministic or stochastic action sampling (selection).
class_docstr_tok: ["Evaluates","a","given","policy","by","rolling","it","out","and","collecting","the","mean","reward",".",":","param","eval_env",":","Distributed","environment","to","run","evaluation","rollouts","in",".",":","param","n_episodes",":","Number","of","evaluation","episodes","to","run",".","Note","that","the","actual","number","might","be","slightly","larger","due","to","the","distributed","nature","of","the","environment",".",":","param","model_selection",":","Model","selection","to","notify","about","the","recorded","rewards",".",":","param","deterministic",":","deterministic","or","stochastic","action","sampling","(","selection",")","."]
code: def __init__(
      self,
      eval_env: StructuredVectorEnv,
      n_episodes: int,
      model_selection: Optional[ModelSelectionBase],
      deterministic: bool = False,
  ):
      self.eval_env = eval_env
      self.n_episodes = n_episodes
      self.model_selection = model_selection
      self.deterministic = deterministic
  
file_path: maze/maze/train/trainers/common/evaluators/rollout_evaluator.py
imports: from typing import Optional
  import numpy
  from maze.core.agent.torch_policy import TorchPolicy
  from maze.core.annotations import override
  from maze.core.env.base_env_events import BaseEnvEvents
  from maze.core.log_stats.log_stats import LogStatsLevel
  from maze.train.parallelization.vector_env.structured_vector_env import StructuredVectorEnv
  from maze.train.trainers.common.evaluators.evaluator import Evaluator
  from maze.train.trainers.common.model_selection.model_selection_base import ModelSelectionBase
label_desc: Use image node_id 1 to create a new RolloutEvaluator object from inherited base classes: Evaluator with example: obj = RolloutEvaluator(eval_env, n_episodes, model_selection, deterministic)