mlsanityData to 14 Oct 2026

Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning

arXiv 2309.10129 ↗First posted 18 Sep 2023cs.AIcited by 6

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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.

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

10 of 21 references.

References · 10

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 58
  5. 2019An intelligent financial portfolio trading strategy using deep Q-learningcited by 14
  6. 2021Strategic Liquidity Provision in Uniswap v3cited by 11
  7. 2022Automated Market Making and Loss-Versus-Rebalancingcited by 10
  8. 2022Differential Liquidity Provision in Uniswap v3 and Implications for Contract Designcited by 6
  9. 2023Uniswap Liquidity Provision: An Online Learning Approachcited by 5
  10. 2022Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2024The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexitycited by 21
  2. 2025Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretrainingcited by 7
  3. 2025Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approachescited by 4
  4. 2025Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decodingcited by 2
  5. 2025Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mambacited by 0
  6. 2026RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMscited by 0