  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.
