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In recent years, decentralized finance (DeFi) has experienced remarkablegrowth, with various protocols such as lending protocols and automated marketmakers (AMMs) emerging. Traditionally, these protocols employ off-chaingovernance, where token holders vote to modify parameters. However, manualparameter adjustment, often conducted by the protocol's core team, isvulnerable to collusion, compromising the integrity and security of the system.Furthermore, purely deterministic, algorithm-based approaches may expose theprotocol to novel exploits and attacks.  In this paper, we present "Auto.gov", a learning-based on-chain governanceframework for DeFi that enhances security and reduces susceptibility toattacks. Our model leverages a deep Q- network (DQN) reinforcement learningapproach to propose semi-automated, intuitive governance proposals withquantitative justifications. This methodology enables the system to efficientlyadapt to and mitigate the negative impact of malicious behaviors, such as priceoracle attacks, more effectively than benchmark models. Our evaluationdemonstrates that Auto.gov offers a more reactive, objective, efficient, andresilient solution compared to existing manual processes, thereby significantlybolstering the security and, ultimately, enhancing the profitability of DeFiprotocols.

Auto.gov: Learning-based On-chain Governance for Decentralized Finance (DeFi)