Newest papers

Cooperative Multi-Agent Reinforcement Learning for Market Making on Prediction-Market Order Books

Skander Lejmi · 2026 · arXiv

DOI: 10.48550/arXiv.2610.26789 · open-access PDF

Japanese

市場メーカーは、資産の購入と販売の価格を引用して流動性を提供し、スプレッドを稼ぎながら在庫リスクを管理します。我々は、予測市場で複数のエージェントの強化学習で訓練された市場メーカー、特にPolyMarketを研究し、その薄い順序ブックは異なる課題を提起しています。この研究を支える3つの課題:不利な選択は、実際のデータで訓練されたポリシーが引用を止めることを引き起こす可能性があります。

Machine translation (Meta M2M100); the English original follows.

Abstract

A market maker provides liquidity by quoting prices to buy and sell an asset, earning the spread while managing inventory risk. We study market makers trained with multi-agent reinforcement learning on prediction markets, specifically Polymarket, whose thin order books pose distinct challenges. Three challenges underpin this research: adverse selection may cause a policy trained on real data to stop quoting; the consequences of a quote can arrive many steps after the action; and prediction-market L2 data does not identify individual orders needed for simulation. We address these challenges by

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