Approximating Auction Equilibria with Reinforcement Learning

Pranjal Rawat

Papers from arXiv.org

Abstract: Traditional methods for computing equilibria in auctions become computationally intractable as auction complexity increases, particularly in multi-item and dynamic auctions. This paper introduces a self-play based reinforcement learning approach that employs advanced algorithms such as Proximal Policy Optimization and Neural Fictitious Self-Play to approximate Bayes-Nash equilibria. This framework allows for continuous action spaces, high-dimensional information states, and delayed payoffs. Through self-play, these algorithms can learn robust and near-optimal bidding strategies in auctions with known equilibria, including those with symmetric and asymmetric valuations, private and interdependent values, and multi-round auctions.

Date: 2024-10
New Economics Papers: this item is included in nep-cmp, nep-des and nep-gth
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