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

TL;DR

Auto.gov introduces a reinforcement learning-based governance system for DeFi that automates parameter adjustments, improving security and profitability over traditional manual or static methods.

Contribution

The paper presents Auto.gov, a novel deep Q-network reinforcement learning framework for semi-automated DeFi governance, demonstrating superior performance and resilience against market manipulations.

Findings

Auto.gov retains funds better against oracle attacks.

Auto.gov outperforms benchmark approaches by at least 14%.

Auto.gov is ten times more profitable than static baseline models.

Abstract

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "Auto$.$gov", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where Auto$.$gov has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, Auto$.$gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric--protocol profitability. Overall, the comprehensive evaluations confirm that Auto$.$gov is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols.