Reinforcement learning methods in board and MOBA games

Abstract

This article provided an introduction of applying reinforcement learning to games, including board games and video games like Backgammon, Go, and Dota2. The reason for choosing reinforcement learning to solve game problems was analyzed. The article also reviewed the reinforcement learning technique and introduced two optimizing learning methods, Temporal Difference learning and Q learning. Then, three important cases of using reinforcement learning to reach high level game skill, TD-gammon, AlphaGo, and OpenAI Five, were introduced. In the end, the future possibility of applying reinforcement learning in broader way was analyzed.