Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning

ArXiv ID: 2501.07508 “View on arXiv”

Authors: Unknown

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.

Keywords: Deep Reinforcement Learning (DRL), Proximal Policy Optimization (PPO), Markov Decision Process (MDP), Concentrated Liquidity, DeFi, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like Markov Decision Processes, Proximal Policy Optimization, and Loss-Versus-Rebalancing modeling, indicating high math complexity. It uses a rolling window approach on real-world market data for training and testing, with a concrete comparison against heuristics, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal:\nOptimize Liquidity Provision in Uniswap v3\nfor Improved DeFi Accessibility"] --> B["Model Formulation:\nMarkov Decision Process (MDP)"]
    B --> C["Core Methodology:\nDeep Reinforcement Learning (PPO Algorithm)"]
    C --> D["Data Inputs:\nHistorical Price Dynamics\nMarket Regime Shifts"]
    D --> E["Computational Process:\nActive LP Agent Training via Rolling Window"]
    E --> F["Key Findings:\nDRL Strategy Outperforms Static Heuristics\nFee Maximization & Impermanent Loss Mitigation"]