Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach to optimize liquidity provisioning in DeFi protocols, aiming to enhance market efficiency and accessibility by dynamically managing liquidity positions based on market conditions.

Contribution

It presents a novel DRL-based strategy for liquidity management in Uniswap v3, outperforming traditional heuristics and promoting more inclusive DeFi participation.

Findings

DRL agent effectively balances fee maximization and loss mitigation.

Compared to heuristics, the DRL approach improves liquidity efficiency.

The method adapts to market regime shifts using a rolling window training approach.

Abstract

This paper applies deep reinforcement learning (DRL) to optimize liquidity provisioning in Uniswap v3, a decentralized finance (DeFi) protocol implementing an automated market maker (AMM) model with concentrated liquidity. We model the liquidity provision task as a Markov Decision Process (MDP) and train an active liquidity provider (LP) agent using the Proximal Policy Optimization (PPO) algorithm. The agent dynamically adjusts liquidity positions by using information about price dynamics to balance fee maximization and impermanent loss mitigation. We use a rolling window approach for training and testing, reflecting realistic market conditions and regime shifts. This study compares the data-driven performance of the DRL-based strategy against common heuristics adopted by small retail LP actors that do not systematically modify their liquidity positions. By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants. Through a data-driven approach to liquidity management, this work seeks to contribute to the ongoing development of more efficient and user-friendly DeFi markets.