标题： 基于规范的学习高社会福利纳什均衡 显示英文标题

标题： Specification-Guided Learning of Nash Equilibria with High Social Welfare

摘要： 强化学习已被证明是自动训练具有挑战性控制问题策略的有效策略。 专注于非合作多智能体系统，我们提出了一种新的强化学习框架，用于训练形成纳什均衡的联合策略。 在我们的方法中，用户不是提供低层次的奖励函数，而是提供高层次的规范，这些规范编码了每个智能体的目标。 然后，在规范结构的指导下，我们的算法搜索策略以确定一个可证明形成$\epsilon$-纳什均衡（高概率）的策略。 重要的是，它以最大化所有智能体社会福利的方式优先选择策略。 我们的实验评估表明，我们的算法计算出的社会福利高的均衡策略，而最先进的基线方法要么无法计算纳什均衡，要么计算出的社会福利相对较低。 显示英文摘要

摘要： Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning framework for training joint policies that form a Nash equilibrium. In our approach, rather than providing low-level reward functions, the user provides high-level specifications that encode the objective of each agent. Then, guided by the structure of the specifications, our algorithm searches over policies to identify one that provably forms an $\epsilon$-Nash equilibrium (with high probability). Importantly, it prioritizes policies in a way that maximizes social welfare across all agents. Our empirical evaluation demonstrates that our algorithm computes equilibrium policies with high social welfare, whereas state-of-the-art baselines either fail to compute Nash equilibria or compute ones with comparatively lower social welfare.