Final Adaptation Reinforcement Learning for N-Player Games
@article{Konen2021FinalAR,
title={Final Adaptation Reinforcement Learning for N-Player Games},
author={W. Konen and Samineh Bagheri},
journal={ArXiv},
year={2021},
volume={abs/2111.14375}
}This paper covers n-tuple-based reinforcement learning (RL) algorithms for games. We present new algorithms for TD-, SARSAand 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…
3 Citations
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