In recent years, decentralized finance (DeFi) has experienced remarkable growth, with various protocols such as lending protocols and automated market makers (AMMs) emerging. Traditionally, these protocols employ off-chain governance, where token holders vote to modify parameters. However, manual parameter adjustment, often conducted by the protocol's core team, is vulnerable to collusion, compromising the integrity and security of the system. Furthermore, purely deterministic, algorithm-based approaches may expose the protocol to novel exploits and attacks. In this paper, we present "Auto.gov", a learning-based on-chain governance framework for DeFi that enhances security and reduces susceptibility to attacks. Our model leverages a deep Q- network (DQN) reinforcement learning approach to propose semi-automated, intuitive governance proposals with quantitative justifications. This methodology enables the system to efficiently adapt to and mitigate the negative impact of malicious behaviors, such as price oracle attacks, more effectively than benchmark models. Our evaluation demonstrates that Auto.gov offers a more reactive, objective, efficient, and resilient solution compared to existing manual processes, thereby significantly bolstering the security and, ultimately, enhancing the profitability of DeFi protocols.

翻译：近年来，去中心化金融（DeFi）经历了显著增长，催生了借贷协议和自动做市商（AMM）等多种协议。传统上，这些协议采用链下治理方式，即代币持有者通过投票修改参数。然而，通常由协议核心团队进行的手动参数调整易受合谋攻击，危及系统的完整性与安全性。此外，纯粹确定性的算法方法可能使协议面临新型漏洞与攻击。本文提出“Auto.gov”——一种基于学习的DeFi链上治理框架，可增强安全性并降低受攻击风险。该模型利用深度Q网络（DQN）强化学习方法，提出半自动化的、可直观理解的治理提案，并附带量化依据。与基准模型相比，此方法使系统能更高效地适应并缓解恶意行为（如价格预言机攻击）的负面影响。评估结果表明，与现有手动流程相比，Auto.gov提供了更具响应性、客观性、高效性和鲁棒性的解决方案，从而显著增强DeFi协议的安全性，并最终提升其盈利能力。