用强化学习优化DeFi借贷利率，提升资金效率与抗风险能力。
From Rules to Rewards: Reinforcement Learning for Interest Rate Adjustment in DeFi Lending
- 采用离线强化学习，基于历史数据自动调整借贷利率。
- TD3-BC模型在利用率、稳定性与风险间取得最佳平衡，表现最优。
- 可应对市场暴跌等极端事件，适合自动化去中心化治理场景。
去中心化金融（DeFi）借贷通过智能合约实现无许可借贷，但面临利率优化难、坏账风险高、资本效率低等问题。基于规则的利率模型难以适应动态市场，导致效率低下。本文将离线强化学习（Offline RL）应用于DeFi借贷协议的利率调整。利用Aave协议的历史数据，评估了三种方法：保守Q学习（CQL）、行为克隆（BC）和带行为克隆的TD3（TD3-BC）。TD3-BC在平衡资金利用率、资本稳定性和风险方面表现最优，优于现有模型。该模型能有效适应历史重大市场压力事件，如2021年5月崩盘和2023年3月USDC脱钩，展现出自动化实时治理的潜力。
原文摘要 · Abstract (English)
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.
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