class_docstr: Ant Environment that randomizes goals as tasks
  
  Goals are randomly sampled 2D positions
class_docstr_tok: ["Ant","Environment","that","randomizes","goals","as","tasks","Goals","are","randomly","sampled","2D","positions"]
code: def __init__(self):
      self.set_task(self.sample_tasks(1)[0])
      MujocoEnv.__init__(self, "ant.xml", 5)
      EzPickle.__init__(self)
  
file_path: ray/rllib/examples/env/ant_rand_goal.py
imports: from gymnasium.envs.mujoco.mujoco_env import MujocoEnv
  from gymnasium.utils import EzPickle
  import numpy
  from ray.rllib.env.apis.task_settable_env import TaskSettableEnv
label_desc: Use image node_id 1 to create a new AntRandGoalEnv object from inherited base classes: EzPickle, MujocoEnv, TaskSettableEnv with example: obj = AntRandGoalEnv()