Multi-Agent Reinforcement Learning for Dynamic Resource Allocation in Real-Time Bidding Platforms

Abstract

Real-time bidding platforms have revolutionized digital advertising by enabling advertisers to dynamically bid for ad impressions in milliseconds. However, efficient resource allocation in such platforms remains challenging due to the highly competitive and dynamic nature of auction environments, where multiple advertisers simultaneously compete for limited advertising opportunities. This paper proposes a Multi-Agent Reinforcement Learning framework for dynamic resource allocation in RTB platforms, where autonomous agents represent individual advertisers optimizing their bidding strategies while considering budget constraints and market competition. The framework employs a distributed coordination mechanism that balances competition and cooperation among agents, enabling them to learn optimal policies through interaction with the auction environment. We formulate the resource allocation problem as a multi-agent Markov Decision Process and develop a novel coordination algorithm that combines Deep Deterministic Policy Gradient with attention-based communication protocols. Our experimental evaluation demonstrates that the proposed approach achieves superior performance compared to traditional single-agent methods, improving click-through rates by an average of 23.5% while maintaining budget constraints and reducing cost-per-click by 18.7%. The results indicate that multi-agent coordination significantly enhances resource utilization efficiency in dynamic RTB environments, providing a scalable solution for complex advertising platforms.