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Automated Market Makers (AMMs) have cemented themselves as an integral partof the decentralized finance (DeFi) space. AMMs are a type of exchange thatallows users to trade assets without the need for a centralized exchange. Theyform the foundation for numerous decentralized exchanges (DEXs), which helpfacilitate the quick and efficient exchange of on-chain tokens. All present-daypopular DEXs are static protocols, with fixed parameters controlling the feeand the curvature - they suffer from invariance and cannot adapt to quicklychanging market conditions. This characteristic may cause traders to stay awayduring high slippage conditions brought about by intractable market movements.We propose an RL framework to optimize the fees collected on an AMM protocol.In particular, we develop a Q-Learning Agent for Market Making Protocols(QLAMMP) that learns the optimal fee rates and leverage coefficients for agiven AMM protocol and maximizes the expected fee collected under a range ofdifferent market conditions. We show that QLAMMP is consistently able tooutperform its static counterparts under all the simulated test conditions.

QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols