Learning to Bipedal Walking on Simulation with SUNA

Abstract

SUNA is currently one of the most adaptive neuroevolution methods which is able to tackle different problems efficiently. However, many questions remain unanswered. In this research, we applied SUNA to the bipedal-walking problem and evaluate it general learning properties. The results show that even without any modificiations SUNA is able to learn in this environment. Moreover, contrary to many other methods, it is continuously improving its average rewards showing a near open-ended learning.