class_docstr: Noop wrapper for gym.Env.
  
  It samples initial states by taking random number of no-ops on reset.
  No-op is assumed to be action 0.
  
  Args:
      env (gym.Env): The environment to be wrapped.
      noop_max (int): Maximum number no-op to be performed on reset.
class_docstr_tok: ["Noop","wrapper","for","gym.Env",".","It","samples","initial","states","by","taking","random","number","of","no-ops","on","reset",".","No-op","is","assumed","to","be","action","0",".","Args",":","env","(","gym.Env",")",":","The","environment","to","be","wrapped",".","noop_max","(","int",")",":","Maximum","number","no-op","to","be","performed","on","reset","."]
code: def __init__(self, env, noop_max=30):
      super().__init__(env)
      self._noop_max = noop_max
      self._noop_action = 0
      assert noop_max > 0, "noop_max should be larger than 0!"
      assert (
          env.unwrapped.get_action_meanings()[0] == "NOOP"
      ), "No-op should be the 0-th action but it's not in {}!".format(
          env
      )
  
imports: import gym
  import numpy
label_desc: Use image node_id 1 to create a new Noop object from inherited base classes: gym with example: obj = Noop(env, noop_max)