Final Adaptation Reinforcement Learning for N-Player Games

2021-11-29 · Wolfgang Konen, Samineh Bagheri

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.

📄 PDF Abstract BibTeX arXiv:2111.14375

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Board GamesQ-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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