Impact of Decentralized Learning on Player Utilities in Stackelberg Games

arXiv:2403.00188v1 Announce Type: new
Abstract: When deployed in the world, a learning agent such as a recommender system or a chatbot often repeatedly interacts with another learning agent (such as a user) over time. In many such two-agent systems, each agent learns separately and the rewards of the two agents are not perfectly aligned. To better understand such cases, we examine the learning dynamics of the two-agent system and the implications for each agent's objective. We model these systems as …

abstract agent agents arxiv chatbot cs.gt cs.lg decentralized games impact systems type utilities world