2021/11/29 by Wolfgang Konen, Konen, Wolfgang, Samineh Bagheri +1
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multiagent Systems (cs.MA) #Reinforcement Learning in Robotics #cs.AI #cs.LG #cs.MA #stat.ML
paper · pdf · doi:10.48550/arxiv.2111.14375
23 pages
arxiv created 2021/11/29 · openalex publication_date 2021/11/29 · arxiv updated 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper covers n-tuple-based reinforcement learning (RL) algorithms for games. We present new algorithms for TD-, SARSA- and Q-learning which work seamlessly on various games with arbitrary number of players. This is achieved by taking a player-centered view where each player propagates his/her rewards back to previous rounds. We add a new element called Final Adaptation RL (FARL) to all these algorithms. Our main contribution is that FARL is a vitally important ingredient to achieve success with the player-centered view in various games. We report results on seven board games with 1, 2 and 3 players, including Othello, ConnectFour and Hex. In most cases it is found that FARL is important to learn a near-perfect playing strategy. All algorithms are available in the GBG framework on GitHub.