class_docstr: Reward shaping wrapper for fixed target speed
class_docstr_tok: ["Reward","shaping","wrapper","for","fixed","target","speed"]
code: def __init__(
      self,
      env,
      act_penalty_lowerbound,
      act_penalty_coeff,
      vel_penalty_coeff,
  ):
      RewardShaping.__init__(self, env)
  
      assert act_penalty_lowerbound is not None
      assert act_penalty_coeff is not None
      assert vel_penalty_coeff is not None
  
      self.act_penalty_lowerbound = act_penalty_lowerbound
      self.act_penalty_coeff = act_penalty_coeff
      self.vel_penalty_coeff = vel_penalty_coeff
  
imports: import abc
  import gym
  import math
  import numpy
  import random
  from collections import OrderedDict
  from osim.env import ProstheticsEnv
  from parl.utils import logger
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new Round2Reward object from inherited base classes: RewardShaping with example: obj = Round2Reward(env, act_penalty_lowerbound, act_penalty_coeff, vel_penalty_coeff)