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

Key points

• A market maker provides liquidity by quoting prices to buy and sell an asset, earning the spread while managing inventory risk.1
• We address these challenges by reconstructing order-level LOBSTER-format data from Polymarket and training agents in JaxMARL-HFT on 417 real markets.1
• We enrich the baseline market maker's observation space with the book and recent flow, redesign its actions and inventory controls, and propose a cooperative multi-agent decomposition into a quoter and a risk manager trained using recurrent Independent Proximal Policy Optimisation (IPPO) with RNN policies.1
• We investigate reward shaping, particularly reward blending and post-fill markout, to mitigate credit-assignment difficulties between the agents.1

Sources (1)

• [1]Cooperative Multi-Agent Reinforcement Learning for Market Making on Prediction-Market Order Books
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:38 PM

  We study market makers trained with multi-agent reinforcement learning on prediction markets, specifically Polymarket, whose thin order books pose distinct challenges.

  A market maker provides liquidity by quoting prices to buy and sell an asset, earning the spread while managing inventory risk.

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