class_docstr: Half-cheetah environment with target velocity, as described in [1].
  
  The code is adapted from
  https://github.com/cbfinn/maml_rl/blob/9c8e2ebd741cb0c7b8bf2d040c4caeeb8e06cc95/rllab/envs/mujoco/half_cheetah_env_rand.py
  
  The half-cheetah follows the dynamics from MuJoCo [2], and receives at each
  time step a reward composed of a control cost and a penalty equal to the
  difference between its current velocity and the target velocity. The tasks
  are generated by sampling the target velocities from the uniform
  distribution on [0, 2].
  
  [1] Chelsea Finn, Pieter Abbeel, Sergey Levine, "Model-Agnostic
      Meta-Learning for Fast Adaptation of Deep Networks", 2017
      (https://arxiv.org/abs/1703.03400)
  [2] Emanuel Todorov, Tom Erez, Yuval Tassa, "MuJoCo: A physics engine for
      model-based control", 2012
      (https://homes.cs.washington.edu/~todorov/papers/TodorovIROS12.pdf)
  
  Args:
      task (dict or None):
          velocity (float): Target velocity, usually between 0 and 2.
docstr: Reset with a task.
  
  Args:
      task (dict[str, float]): A task (a dictionary containing a single
          key, "velocity", usually between 0 and 2).
docstr_tok: ["Reset","with","a","task",".","Args",":","task","(","dict","[","str",",","float","]",")",":","A","task","(","a","dictionary","containing","a","single","key",",","``","velocity","''",",","usually","between","0","and","2",")","."]
code: def set_task(self, task):
      """Reset with a task.
  
      Args:
          task (dict[str, float]): A task (a dictionary containing a single
              key, "velocity", usually between 0 and 2).
  
      """
      self._task = task
  
imports: import numpy
  from garage.envs.mujoco.half_cheetah_env_meta_base import HalfCheetahEnvMetaBase
label_desc: Use image node_id 4 for calling the HalfCheetahVelEnv obj's underlying member method code with example usage: obj.set_task(task) without return types