Game Redesign in No-regret Game Playing

Yuzhe Ma; Young Wu; Xiaojin Zhu

IJCAI 2022poster10 citations

Game Redesign in No-regret Game Playing

Abstract

We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the original game. The players apply no-regret learning algorithms to repeatedly play the changed games with limited feedback. The goals of the designer are to (i) incentivize players to take a specific target action profile frequently; (ii) incur small cumulative design cost. We present game redesign algorithms with the guarantee that the target action profile is played in T-o(T) rounds while incurring only o(T) cumulative design cost. Simulations on four classic games confirm the ef- fectiveness of our proposed redesign algorithms.

Machine Learning: Adversarial Machine LearningAgent-based and Multi-agent Systems: Multi-agent Learning

BibTeX

@inproceedings{ijcai2022p461,
  title     = {Game Redesign in No-regret Game Playing},
  author    = {Ma, Yuzhe and Wu, Young and Zhu, Xiaojin},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {3321--3327},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/461},
  url       = {https://doi.org/10.24963/ijcai.2022/461},
}