We present Toribash Learning Environment (ToriLLE), a learning environment for machine learning agents based on the video game Toribash. Toribash is a MuJoCo-like environment of two humanoid character fighting each other hand-to-hand, controlled by changing actuation modes of the joints. Competitive nature of Toribash as well its focused domain provide a platform for evaluating self-play methods, and evaluating machine learning agents against human players. In this paper we describe the environment with ToriLLE's capabilities and limitations, and experimentally show its applicability as a learning environment. The source code of the environment and conducted experiments can be found at https://github.com/Miffyli/ToriLLE.
Cite
@article{arxiv.1807.10110,
title = {ToriLLE: Learning Environment for Hand-to-Hand Combat},
author = {Anssi Kanervisto and Ville Hautamäki},
journal= {arXiv preprint arXiv:1807.10110},
year = {2019}
}
Comments
https://github.com/Miffyli/ToriLLE . Accepted to IEEE Conference on Games 2019