Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918059690688512 |
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| author | Gemp, Ian Haupt, Andreas Marris, Luke Liu, Siqi Piliouras, Georgios |
| author_facet | Gemp, Ian Haupt, Andreas Marris, Luke Liu, Siqi Piliouras, Georgios |
| contents | Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across time. We introduce the class of convex Markov games that allow general convex preferences over occupancy measures. Despite infinite time horizon and strictly higher generality than Markov games, pure strategy Nash equilibria exist. Furthermore, equilibria can be approximated empirically by performing gradient descent on an upper bound of exploitability. Our experiments reveal novel solutions to classic repeated normal-form games, find fair solutions in a repeated asymmetric coordination game, and prioritize safe long-term behavior in a robot warehouse environment. In the prisoner's dilemma, our algorithm leverages transient imitation to find a policy profile that deviates from observed human play only slightly, yet achieves higher per-player utility while also being three orders of magnitude less exploitable. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_16600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning Gemp, Ian Haupt, Andreas Marris, Luke Liu, Siqi Piliouras, Georgios Computer Science and Game Theory Artificial Intelligence Multiagent Systems Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across time. We introduce the class of convex Markov games that allow general convex preferences over occupancy measures. Despite infinite time horizon and strictly higher generality than Markov games, pure strategy Nash equilibria exist. Furthermore, equilibria can be approximated empirically by performing gradient descent on an upper bound of exploitability. Our experiments reveal novel solutions to classic repeated normal-form games, find fair solutions in a repeated asymmetric coordination game, and prioritize safe long-term behavior in a robot warehouse environment. In the prisoner's dilemma, our algorithm leverages transient imitation to find a policy profile that deviates from observed human play only slightly, yet achieves higher per-player utility while also being three orders of magnitude less exploitable. |
| title | Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning |
| topic | Computer Science and Game Theory Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2410.16600 |