Liquidity Optimization in Decentralized Money Markets Through Reinforcement-Driven Smart Contract Agents
Keywords:
decentralized finance, liquidity optimization, reinforcement learning, smart contract agents, money markets, interest rate modeling, systemic stability, autonomous protocolsAbstract
Decentralized money markets are needed in blockchain ecosystems, but liquidity dispersion, poor interest rate dynamics, and external shocks and endogenous feedback loops make them vulnerable. This paper optimizes decentralized lending protocol liquidity using reinforcement learning–driven autonomous smart contract agents. On-chain agents modify borrowing rates, reserve factors, collateral incentives, and exposure limitations to market conditions in sequential liquidity management. Reinforcement learning algorithms optimize control policies to reduce insolvency, cascading liquidations, and liquidity depletion risk indicators and improve protocol usability. The paper builds a stability-aware reward function that internalizes systemic risk to prevent local optimization from damaging global markets. Permissionless smart contract learning agent deployments address state observability, computational limits, governance alignment, and adversarial resilience. Simulation and theory show that reinforcement-driven agents outperform static or heuristic systems in capital efficiency, utilization ratio volatility, and stress resistance. Results indicate reinforcement learning–enabled smart contracts might allow adaptive, self-regulating decentralized money markets with improved efficiency and systemic stability.
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