Implementation of SparseHumanoid-v2

Environment used in the papers

• Leveraging exploration in off-policy algorithms via normalizing flows (CoRL 2019)

 @article{mazoure2019leveraging,
  title={Leveraging exploration in off-policy algorithms via normalizing flows},
  author={Mazoure, Bogdan and Doan, Thang and Durand, Audrey and Hjelm, R Devon and Pineau, Joelle},
  journal={Proceedings of the 3rd Conference on Robot Learning (CoRL 2019)},
  year={2019}
} 

Information about the environment:

• Episode max steps: 1000

• The episode terminates if the agent falls down

• Reward of +1 is granted if the agent's center of mass (COM) is above a threshold distance (wrt to origin) of 0.6.

How to use

import gym

env=gym.make("SparseHumanoid-v2")