This paper presents an LR-I lagging anchor algorithm that combines a lagging anchor method with an LR-I learning algorithm. We prove that this decentralized learning algorithm converges in strategies to Nash equilibria in two-player two-action general-sum matrix games. A practical LR-I lagging anchor algorithm is introduced for players to learn their Nash equilibrium strategies in general-sum stochastic games. Simulation results show the performance of the proposed LR-I lagging anchor algorithm in both matrix games and stochastic games.

Additional Metadata
Keywords Game theory, Matrix games, Multiagent learning
Journal International Journal of Innovative Computing, Information and Control
Citation
Lu, X. (Xiaosong), & Schwartz, H.M. (2013). Decentralized learning in general-sum matrix games: An LR-Ilagging anchor algorithm. International Journal of Innovative Computing, Information and Control, 9(1), 17–32.