RL for UniswapV3: Paper Proposes New Framework for Dynamic Liquidity Provision
chaumian · x · 2026-08-21
This paper explores using Reinforcement Learning (RL) to solve dynamic liquidity provision in concentrated liquidity Automated Market Makers (AMMs) like Uniswap V3. The research formulates the problem as a stochastic impulse control problem to determine when to rebalance positions and allocate capital across price ranges. Experiments show that learned policies exhibit rich state-dependent behavior, allocating liquidity based on mispricing, rebalancing costs, and uncertainty. This helps compress the left tail of the P&L distribution and avoid catastrophic outcomes under high uncertainty. The method outperforms baseline agents from AMM microstructure literature.
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