code: def __init__(
      self,
      difficulty,
      vel_penalty_coeff,
      muscle_penalty_coeff,
      penalty_coeff,
      only_first_target=False,
  ):
      random_seed = np.random.randint(int(1e9))
  
      env = L2M2019Env(
          difficulty=difficulty, visualize=False, seed=random_seed
      )
      max_timelimit = env.time_limit
  
      env = FinalReward(
          env,
          max_timelimit=max_timelimit,
          vel_penalty_coeff=vel_penalty_coeff,
          muscle_penalty_coeff=muscle_penalty_coeff,
          penalty_coeff=penalty_coeff,
      )
  
      if only_first_target:
          assert (
              difficulty == 3
          ), "argument `only_first_target` is available only in `difficulty=3`."
          env = FirstTarget(env)
  
      env = FrameSkip(env)
      env = ActionScale(env)
      self.env = OfficialObs(env, max_timelimit=max_timelimit)
  
file_path: PARL/examples/NeurIPS2019-Learn-to-Move-Challenge/actor.py
imports: import parl
  import numpy
  from osim.env import L2M2019Env
  from env_wrapper import FrameSkip, ActionScale, OfficialObs, FinalReward, FirstTarget
label_desc: Use image node_id 1 to create a new Actor object from inherited base classes: object with example: obj = Actor(difficulty, vel_penalty_coeff, muscle_penalty_coeff, penalty_coeff, only_first_target)