Strategic learning for disturbance rejection in multi-agent systems: Nash and Minmax in graphical games
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909573779030016 |
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| author | Wang, Xinyang Guay, Martin Wang, Shimin Zhang, Hongwei |
| author_facet | Wang, Xinyang Guay, Martin Wang, Shimin Zhang, Hongwei |
| contents | This article investigates the optimal control problem with disturbance rejection for discrete-time multi-agent systems under cooperative and non-cooperative graphical games frameworks. Given the practical challenges of obtaining accurate models, Q-function-based policy iteration methods are proposed to seek the Nash equilibrium solution for the cooperative graphical game and the distributed minmax solution for the non-cooperative graphical game. To implement these methods online, two reinforcement learning frameworks are developed, an actor-disturber-critic structure for the cooperative graphical game and an actor-adversary-disturber-critic structure for the non-cooperative graphical game. The stability of the proposed methods is rigorously analyzed, and simulation results are provided to illustrate the effectiveness of the proposed methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Strategic learning for disturbance rejection in multi-agent systems: Nash and Minmax in graphical games Wang, Xinyang Guay, Martin Wang, Shimin Zhang, Hongwei Systems and Control This article investigates the optimal control problem with disturbance rejection for discrete-time multi-agent systems under cooperative and non-cooperative graphical games frameworks. Given the practical challenges of obtaining accurate models, Q-function-based policy iteration methods are proposed to seek the Nash equilibrium solution for the cooperative graphical game and the distributed minmax solution for the non-cooperative graphical game. To implement these methods online, two reinforcement learning frameworks are developed, an actor-disturber-critic structure for the cooperative graphical game and an actor-adversary-disturber-critic structure for the non-cooperative graphical game. The stability of the proposed methods is rigorously analyzed, and simulation results are provided to illustrate the effectiveness of the proposed methods. |
| title | Strategic learning for disturbance rejection in multi-agent systems: Nash and Minmax in graphical games |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.07547 |