Abstract
This paper investigates distributed online Nash equilibrium learning with privacy preservation in dynamic non-cooperative games. In this context, players optimize their actions under local constraints to track the time-varying equilibrium without access to future cost information while protecting their private strategy information. To this end, a novel privacy-preserving distributed online algorithm with two-point bandit feedback is proposed, which enables Nash equilibrium tracking using only local function evaluations. Moreover, theoretical analysis establishes sublinear dynamic regret under sublinear path variation. Finally, numerical simulations validate theoretical results.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025 |
| Editors | Rong SONG |
| Publisher | IEEE |
| Pages | 1568-1573 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798331526726 |
| ISBN (Print) | 9798331526733 |
| DOIs | |
| Publication status | Published - 2025 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Funding
This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFA1004702, in part by the National Natural Science Foundation of China through Grant 62325304, Grant U22B2046, Grant 62073079, and Grant 62088101, and in part by the Jiangsu Provincial Scientific Research Center of Applied Mathematics under Grant BK20233002.
Keywords
- Nash equilibrium seeking
- bandit feedback
- privacy preservation
- decentralized online game
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