Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games (2002)
| Venue: | in Advances in Neural Information Processing Systems |
| Citations: | 57 - 3 self |
BibTeX
@INPROCEEDINGS{Wang02reinforcementlearning,
author = {Xiaofeng Wang and Tuomas Sandholm},
title = {Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games},
booktitle = {in Advances in Neural Information Processing Systems},
year = {2002},
pages = {1571--1578},
publisher = {MIT Press}
}
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Abstract
Multiagent learning is a key problem in game theory and AI. It involves two interrelated learning problems: identifying the game and learning to play. These two problems prevail even in team games where the agents' interests do not conflict. Even team games can have multiple Nash equilibria, only some of which are optimal. We present optimal adaptive learning (OAL), the first algorithm that converges to an optimal Nash equilibrium for any team Markov game. We provide a convergence proof, and show that the algorithm's parameters are easy to set so that the convergence conditions are met. Our experiments show that existing algorithms do not converge in many of these problems while OAL does. We also demonstrate the importance of the fundamental ideas behind OAL: incomplete history sampling and biased action selection.
