class_docstr: This class evaluates policy of OpenAI Gym environment.
  
  Parameters
  -----------
  env_name: str
      Gym environment name
  state_mean: list
      Average state values of multiple independent runs.
  state_std: list
      Standard deviation of state values of multiple independent runs.
  num_rollouts: int
      number of independent runs.
  activation: str:
      activation function
  layer_rescaling_coef: float
      Scaling coefficient of output layers
  random_state: int or None
      random state for reproducibility in Gym environment.
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code: def __init__(
      self,
      env_name,
      state_mean,
      state_std,
      num_rollouts,
      activation,
      layer_rescaling_coef,
      noise_level,
      random_state,
  ):
      self.mean = state_mean
      self.std = state_std
      self.env = gym.make(env_name)
      self.num_rollouts = num_rollouts
      self.random_state = random_state
      self.activation = activation
      self.layer_rescaling_coef = layer_rescaling_coef
      self.noise_level = noise_level
  
imports: import gym
  import numpy
label_desc: Use image node_id 1 to create a new GenericMujocoEnv object  with example: obj = GenericMujocoEnv(env_name, state_mean, state_std, num_rollouts, activation, layer_rescaling_coef, noise_level, random_state)