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AI agents trained for prediction market making on Polymarket

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a cooperative multi-agent reinforcement learning system for market making on prediction markets, specifically addressing challenges posed by Polymarket's thin order books. The system reconstructs order-level data and trains agents using Independent Proximal Policy Optimisation with recurrent neural network policies. While the redesigned single-agent market maker showed improved quoting frequency and reduced losses compared to a baseline, none of the configurations achieved positive average portfolio value in evaluations. AI

IMPACT Explores novel applications of multi-agent reinforcement learning in financial markets, potentially influencing algorithmic trading strategies.

RANK_REASON Academic paper detailing a novel application of multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents trained for prediction market making on Polymarket

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Academic paper detailing a novel application of multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Skander Lejmi ·

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

    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 disti…

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