Training a Reinforcement Learning Agent based on XCS in a Competitive Snake Environment

In contrast to neural networks, learning classifier systems are no “black box” algorithm. They provide rule-based artificial intelligence, which can easily be analysed, interpreted and even adapted by humans. We constructed an agent based on such a learning classifier system and trained it to play in a competitive snake environment by utilizing reinforcement learning and self-play methods. Our preliminary experiments show promising results that we plan to extend on in the future.