Generalized Individual Q-learning for Polymatrix Games with Partial Observations
<|reference_start|>Generalized Individual Q-learning for Polymatrix Games with Partial Observations: This paper addresses the challenge of limited observations in non-cooperative multi-agent systems where agents can have partial access to other agents' actions. We present the generalized individual Q-learning dynamics that combine belief-based and payoff-based learning for the networked interconnections of more than two self-interested agents. This approach leverages access to opponents' actions whenever possible, demonstrably achieving a faster (guaranteed) convergence to quantal response equilibrium in multi-agent zero-sum and potential polymatrix games. Notably, the dynamics reduce to the well-studied smoothed fictitious play and individual Q-learning under full and no access to opponent actions, respectively. We further quantify the improvement in convergence rate due to observing opponents' actions through numerical simulations.<|reference_end|>
@article{donmez2024generalized,
    title={Generalized Individual Q-learning for Polymatrix Games with Partial
  Observations},
    author={Ahmed Said Donmez and Muhammed O. Sayin},
    journal={arXiv preprint arXiv:2409.02663},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02663},
    primaryClass={cs.GT cs.SY eess.SY}
}