/sparseMuJoCo

Sparse environment for MuJoCo suite (v2 and v3)

Primary LanguagePython

Implementation of SparseHumanoid-v2

Environment used in the papers

 @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")