RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs
This paper introduces RAmmStein, a deep reinforcement learning framework that optimizes liquidity provision in concentrated Automated Market Makers by solving an impulse control problem via a Hamilton-Jacobi-Bellman quasi-variational inequality, thereby significantly reducing rebalancing frequency and gas costs while maximizing net returns through regime-aware, mean-reversion-informed decision-making.