# Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2501.07508 · HTML: https://mlsanity.com/paper/2501.07508/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 8 of 8)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols](https://mlsanity.com/paper/2103.12732/) (2021)
- [Strategic Liquidity Provision in Uniswap v3](https://mlsanity.com/paper/2106.12033/) (2021)
- [Automated Market Making and Loss-Versus-Rebalancing](https://mlsanity.com/paper/2208.06046/) (2022)
- [Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design](https://mlsanity.com/paper/2204.00464/) (2022)
- [An Empirical Study of Market Inefficiencies in Uniswap and SushiSwap](https://mlsanity.com/paper/2203.07774/) (2022)
- [Complexity-Approximation Trade-offs in Exchange Mechanisms: AMMs vs. LOBs](https://mlsanity.com/paper/2302.11652/) (2023)

## Built on by (most cited first, 2 of 2)

- [From Rules to Rewards: Reinforcement Learning for Interest Rate Adjustment in DeFi Lending](https://mlsanity.com/paper/2506.00505/) (2025)
- [RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs](https://mlsanity.com/paper/2602.19419/) (2026)

## Abstract (arXiv)

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.