Technical Note: \cal Q-Learning
Machine Learning
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Prisoner's Dilemma
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Utility Based Q-learning to Maintain Cooperation in Prisoner's Dilemma Games
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
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There are many multiagent Q-learning methods, and most of them aim to converge to a Nash equilibrium that is not desirable in games like the Prisoner's Dilemma (PD). The author proposed the utility-based Q-learning (UB-Q) for PD that used utilities instead of rewards so as to maintain mutual cooperation once it had occurred. However, UB-Q has to know the payoffs of the game to calculate the utilities and works only in PD. Since a Q-learning agent's action depends on the relation of Q-values, the mutual cooperation can also be maintained by adjusting the learning rate. Thus, this paper deals with the learning rate directly and introduces another Q-learning method called the learning-rate adjusting Q-learning (LRA-Q). It calculates the learning rate from received payoffs and works in other kinds of two-person two-action symmetric games as well as PD. Numeric verification showed success of LRA-Q, but, it also revealed a side-effect.