Multi-Agent Reinforcement Learning in Markets with Congestion

2026-09-13

AI Summary

研究通过多智能体强化学习解决市场中企业因资源拥堵而竞争客户的问题，分析了学习动态、状态表示和策略互动如何共同影响竞争。

Abstract

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.

Problem

• Multi-Agent Reinforcement Learning
• Congestion
• Bertrand Competition
• Inverse Demand Curve
• Tacit Collusion

Innovation

• Multi-Agent Reinforcement Learning
• Congestion
• Bertrand Competition
• Tacit Collusion
• Inverse Demand Curve