We introduce a new approach for computing optimal equilibria via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that optimal equilibria are minimax equilibrium strategies of a player in an extensive-form zero-sum game. This reformulation allows to apply techniques for learning in zero-sum games, yielding the first learning dynamics that converge to optimal equilibria, not only in empirical averages, but also in iterates. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning.

翻译：我们提出了一种通过博弈学习计算最优均衡的新方法。该方法适用于任意参与人数的扩展式博弈场景，包括机制设计、信息设计以及相关均衡、通信均衡和认证均衡等解概念。我们观察到，最优均衡可视为一个参与者在扩展式零和博弈中的极小极大均衡策略。这一重构使得我们能够应用零和博弈中的学习技术，从而首次实现了不仅能在经验平均意义上、还能在迭代序列中收敛至最优均衡的学习动态。我们通过在基准表格博弈中达到最先进的性能，并利用深度强化学习为序贯拍卖设计问题计算最优机制，展示了该方法在实际可扩展性和灵活性方面的优势。