From Rules to Rewards: Reinforcement Learning for Interest Rate Adjustment in DeFi Lending
This addresses inefficiencies in DeFi lending for users and protocols by automating interest rate adjustments, though it is incremental as it applies known RL methods to a new domain.
The paper tackles the problem of optimizing interest rates in DeFi lending to improve capital efficiency and mitigate risks, applying offline reinforcement learning to historical Aave data, with TD3-BC outperforming existing models by effectively adapting to market stress events like crashes and depegs.
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.