From Rules to Rewards: Reinforcement Learning for Interest Rate Adjustment in DeFi Lending
On the map: Offline RL and imitation learning from data → Offline RL algorithms
Abstract (arXiv)
Decentralized Finance (DeFi) lending enables permissionless borrowing via smart contracts. However, it faces challenges in optimizing interest rates, mitigating bad debt, and improving capital efficiency. Rule-based interest-rate models struggle to adapt to dynamic market conditions, leading to inefficiencies. This work applies Offline Reinforcement Learning (RL) to optimize interest rate adjustments in DeFi lending protocols. Using historical data from Aave protocol, we evaluate three RL approaches: Conservative Q-Learning (CQL), Behavior Cloning (BC), and TD3 with Behavior Cloning (TD3-BC). TD3-BC demonstrates superior performance in balancing utilization, capital stability, and risk, outperforming existing models. It adapts effectively to historical stress events like the May 2021 crash and the March 2023 USDC depeg, showcasing potential for automated, real-time governance.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 22 references that are in the map. First posted on arXiv 31 May 2025.
Builds on
9 of 22 references.
Closest ancestors · 2
Same direction, newest first
- 2021A Minimalist Approach to Offline Reinforcement Learning12 Jun
- 2020Conservative Q-Learning for Offline Reinforcement Learning8 Jun
Other references · 7
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
- 2018Behavioral Cloning from Observationcited by 492
- 2024Reinforcement learningcited by 247
- 2020Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learningcited by 134
- 2022SoK: Decentralized Finance (DeFi) Attackscited by 28
- 2021SoK: Decentralized Finance (DeFi)cited by 23
- 2025Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learningcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
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- 2022Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations
- 2024Reinforcement Learning Framework for Quantitative Trading
- 2024Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
- 2026Counterfactual Analysis via Large Language Models