DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913119652020224 |
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| author | Lee, Duan-Shin Hung, Yu-Hsiu |
| author_facet | Lee, Duan-Shin Hung, Yu-Hsiu |
| contents | In this paper we study team-symmetric games with $m\ge 2$ teams. Players within a team have symmetric identity and have a common payoff function. We show that team-symmetric games always have a team-symmetric Nash equilibrium. We develop and solve a linear complementarity problem of team-symmetric Nash equilibria. We propose an actor-critic based multi-agent reinforcement learning algorithm for team-symmetric games. Through simulations, we show that this multi-agent reinforcement learning algorithm performs much better than many existing algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12555 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games Lee, Duan-Shin Hung, Yu-Hsiu Multiagent Systems Computer Science and Game Theory In this paper we study team-symmetric games with $m\ge 2$ teams. Players within a team have symmetric identity and have a common payoff function. We show that team-symmetric games always have a team-symmetric Nash equilibrium. We develop and solve a linear complementarity problem of team-symmetric Nash equilibria. We propose an actor-critic based multi-agent reinforcement learning algorithm for team-symmetric games. Through simulations, we show that this multi-agent reinforcement learning algorithm performs much better than many existing algorithms. |
| title | DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games |
| topic | Multiagent Systems Computer Science and Game Theory |
| url | https://arxiv.org/abs/2605.12555 |