# Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2309.10129 · HTML: https://mlsanity.com/paper/2309.10129/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [An intelligent financial portfolio trading strategy using deep Q-learning](https://mlsanity.com/paper/1907.03665/) (2019)
- [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)
- [Uniswap Liquidity Provision: An Online Learning Approach](https://mlsanity.com/paper/2302.00610/) (2023)
- [Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning](https://mlsanity.com/paper/2211.01346/) (2022)

## Built on by (most cited first, 6 of 6)

- [The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity](https://mlsanity.com/paper/2412.06148/) (2024)
- [Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining](https://mlsanity.com/paper/2502.05790/) (2025)
- [Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approaches](https://mlsanity.com/paper/2503.01314/) (2025)
- [Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding](https://mlsanity.com/paper/2503.11108/) (2025)
- [Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mamba](https://mlsanity.com/paper/2511.22101/) (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)

Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi), allowing users to trade cryptocurrencies without the need for third-party authorization. Investors are incentivized to deposit assets into liquidity pools, against which users can trade directly, while paying fees to liquidity providers (LPs). However, a number of unresolved issues related to capital efficiency and market risk hinder DeFi's further development. Uniswap V3, a leading and groundbreaking DEX project, addresses capital efficiency by enabling LPs to concentrate their liquidity within specific price ranges for deposited assets. Nevertheless, this approach exacerbates market risk, as LPs earn trading fees only when asset prices are within these predetermined brackets. To mitigate this issue, this paper introduces a deep reinforcement learning (DRL) solution designed to adaptively adjust these price ranges, maximizing profits and mitigating market risks. Our approach also neutralizes price-change risks by hedging the liquidity position through a rebalancing portfolio in a centralized futures exchange. The DRL policy aims to optimize trading fees earned by LPs against associated costs, such as gas fees and hedging expenses, which is referred to as loss-versus-rebalancing (LVR). Using simulations with a profit-and-loss (PnL) benchmark, our method demonstrates superior performance in ETH/USDC and ETH/USDT pools compared to existing baselines. We believe that this strategy not only offers investors a valuable asset management tool but also introduces a new incentive mechanism for DEX designers.