拥塞市场中的多智能体强化学习

中文摘要 AI 辅助

本文研究了企业在使用拥塞性资源争夺客户的环境中进行的多智能体强化学习（MARL）。我们考虑伯特兰竞争，其中企业通过公布价格进行竞争，而客户根据价格和拥塞程度在企业之间进行选择。价格、拥塞程度以及愿意接受服务的客户数量之间的关系由一条未知的逆需求曲线决定，企业必须通过经验来学习该曲线。每个企业被建模为一个自利的学习智能体，其选择价格以最大化利润。越来越多的文献表明，独立学习的MARL智能体可以发展出默契的合谋行为。我们研究了这种行为如何在拥塞性资源市场中产生。我们的结果揭示了学习动态、状态表示和战略互动如何共同塑造竞争，这对经济学习以及支持学习的市场的设计都具有启示意义。

英文摘要

This paper investigates multi-agent reinforcement learning (MARL) in settings where firms compete for customers using congestible resources. We consider Bertrand competition in which firms compete by announcing prices and customers choose among firms based on both price and congestion. The relationship between price, congestion and the quantity of customers willing to accept service is governed by an unknown inverse demand curve, which firms must learn through experience. Each firm is modeled as a self-interested learning agent that chooses its price to maximize profit. A growing literature has shown that independently learning MARL agents can develop tacitly collusive behavior. We examine how such behavior emerges in markets with congestible resources. Our results provide insight into how learning dynamics, state representation, and strategic interaction jointly shape competition, with implications for both economic learning and the design of learning-enabled markets.

发表机构

• Northwestern University（西北大学）

机构由 AI 辅助整理，请以论文原文为准。