Deep Reinforcement Learning for Sequential Combinatorial Auctions

TL;DR

This paper introduces a new reinforcement learning framework tailored for sequential combinatorial auctions, achieving higher revenue and scalability in complex auction scenarios compared to traditional methods.

Contribution

We propose a differentiable transition-based reinforcement learning approach specifically designed for sequential combinatorial auctions, improving revenue and scalability.

Findings

Significant revenue improvement over analytical and RL baselines

Scales to 50 agents and 50 items in complex scenarios

Bridges gap between theory and practical auction design

Abstract

Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and strong strategyproofness guarantees, are often limited by theoretical results that are largely existential, except for certain restrictive settings. Although traditional reinforcement learning methods such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are applicable in this domain, they struggle with computational demands and convergence issues when dealing with large and continuous action spaces. In light of this and recognizing that we can model transitions differentiable for our settings, we propose using a new reinforcement learning framework tailored for sequential combinatorial auctions that leverages first-order gradients. Our extensive evaluations show that our approach achieves significant improvement in revenue over both analytical baselines and standard reinforcement learning algorithms. Furthermore, we scale our approach to scenarios involving up to 50 agents and 50 items, demonstrating its applicability in complex, real-world auction settings. As such, this work advances the computational tools available for auction design and contributes to bridging the gap between theoretical results and practical implementations in sequential auction design.