# Approximating Auction Equilibria with Reinforcement Learning

arXiv: https://arxiv.org/abs/2410.13960 · HTML: https://mlsanity.com/paper/2410.13960/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 1 of 1)

- [Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification](https://mlsanity.com/paper/1812.01955/) (2018)

## Abstract (arXiv)

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.