论文标题
关于在重复游戏中学习与自适应合作伙伴策略合作的不可能
On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games
论文作者
论文摘要
当这些代理人还具有适应我们自己的行为的能力时，学会与其他代理商合作是具有挑战性的。在合作环境中学习的实用和理论方法通常假定其他代理人的行为是静止的，或者对其他代理人的学习过程做出了非常具体的假设。这项工作的目的是了解我们是否可以在没有这种限制性假设的情况下可靠地学会与其他代理进行合作，而这些假设不太可能在现实世界应用中保留。我们的主要贡献是一组不可能的结果，这表明没有学习算法可以可靠地学习与重复的矩阵游戏中所有可能的自适应伙伴合作，即使该合作伙伴可以通过某种固定策略合作。然后，在这些结果的激励下，我们讨论了潜在的替代假设，这些假设捕捉了自适应伴侣只能理性地适应我们的行为的想法。
Learning to cooperate with other agents is challenging when those agents also possess the ability to adapt to our own behavior. Practical and theoretical approaches to learning in cooperative settings typically assume that other agents' behaviors are stationary, or else make very specific assumptions about other agents' learning processes. The goal of this work is to understand whether we can reliably learn to cooperate with other agents without such restrictive assumptions, which are unlikely to hold in real-world applications. Our main contribution is a set of impossibility results, which show that no learning algorithm can reliably learn to cooperate with all possible adaptive partners in a repeated matrix game, even if that partner is guaranteed to cooperate with some stationary strategy. Motivated by these results, we then discuss potential alternative assumptions which capture the idea that an adaptive partner will only adapt rationally to our behavior.
