ToriLLE: Learning Environment for Hand-to-Hand Combat

We present Toribash Learning Environment (To-riLLE), a learning environment for machine learning agents based on the video game Toribash. Toribash is a MuJoCo-like environment of two humanoid characters 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 with baseline and human experiments. The source code of the environment and conducted experiments can be found at https://github.com/Miffyli/ToriLLE.