Turn-based Multi-Agent Reinforcement Learning Model Checking
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910773814493184 |
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| author | Gross, Dennis |
| author_facet | Gross, Dennis |
| contents | In this paper, we propose a novel approach for verifying the compliance of turn-based multi-agent reinforcement learning (TMARL) agents with complex requirements in stochastic multiplayer games. Our method overcomes the limitations of existing verification approaches, which are inadequate for dealing with TMARL agents and not scalable to large games with multiple agents. Our approach relies on tight integration of TMARL and a verification technique referred to as model checking. We demonstrate the effectiveness and scalability of our technique through experiments in different types of environments. Our experiments show that our method is suited to verify TMARL agents and scales better than naive monolithic model checking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Turn-based Multi-Agent Reinforcement Learning Model Checking Gross, Dennis Artificial Intelligence Machine Learning Multiagent Systems In this paper, we propose a novel approach for verifying the compliance of turn-based multi-agent reinforcement learning (TMARL) agents with complex requirements in stochastic multiplayer games. Our method overcomes the limitations of existing verification approaches, which are inadequate for dealing with TMARL agents and not scalable to large games with multiple agents. Our approach relies on tight integration of TMARL and a verification technique referred to as model checking. We demonstrate the effectiveness and scalability of our technique through experiments in different types of environments. Our experiments show that our method is suited to verify TMARL agents and scales better than naive monolithic model checking. |
| title | Turn-based Multi-Agent Reinforcement Learning Model Checking |
| topic | Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2501.03187 |