Auto.gov introduces a learning-based governance framework for decentralized finance (DeFi) protocols, specifically targeting the automated adjustment of risk parameters such as collateral factors in lending protocols like Aave. The framework employs a Deep Q-Network (DQN) reinforcement learning agent that interacts with a stylized DeFi environment modeled as a Markov Decision Process. The environment simulates user actions (deposit, withdraw, borrow, repay, liquidate) and market dynamics, including price oracle attacks. The agent's objective is to maximize the protocol's net position (profitability) by adjusting collateral factors incrementally, with constraints to ensure safety and controllability.

Experimental results demonstrate that Auto.gov significantly outperforms both static baselines and benchmark approaches. In simulated scenarios with price oracle attacks, the RL agent improves protocol profitability by over 60% compared to a baseline agent. In real-world data tests, Auto.gov achieves at least a 14% improvement over the best benchmark and a tenfold improvement over the static baseline. The training process is efficient, converging in about 4 hours on a laptop, which is orders of magnitude faster than traditional week-long governance votes. The framework also enhances security by mitigating risks from market manipulations and human biases, contributing to the sustainability of DeFi protocols. This work represents the first known integration of AI with DeFi governance, offering a scalable and resilient alternative to manual or voting-based parameter adjustments.