用模型检验验证轮换制多智能体强化学习的合规性
Turn-based Multi-Agent Reinforcement Learning Model Checking
- 将轮换制多智能体强化学习与模型检验结合
- 在多种环境中验证了方法的有效性与可扩展性
- 适合需要可靠性的多智能体系统开发人员
本文提出一种新方法，用于验证轮换制多智能体强化学习（TMARL）智能体在随机多人游戏中的复杂需求合规性。现有验证方法无法有效处理TMARL智能体，且难以扩展至多智能体大型游戏。本方法通过紧密集成TMARL与模型检验技术，克服了上述局限。实验表明，该方法能有效验证TMARL智能体，并在不同环境类型中展现出优于传统整体式模型检验的可扩展性。
原文摘要 · Abstract (English)
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.
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