论文标题
预测性加密资产自动化市场制造用于分散融资的架构，使用深度强化学习
Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning
论文作者
论文摘要
该研究提出了一个具有链接和定居功能的报价驱动的预测自动化做市商（AMM）平台，以及链链预测性增强学习能力，以提高现实世界中的流动性提供。拟议的AMM体系结构是通过利用新颖的市场均衡定价来减少发散和滑倒损失的新型市场均衡定价，对Uniswap V3（一种加密货币AMM协议）的增强。此外，提出的体系结构涉及预测性AMM功能，利用深层混合长期短期记忆（LSTM）和Q学习强化学习框架，旨在通过更好地预测流动性浓度范围来提高市场效率，因此流动性开始转向预期的浓度范围，在资产价格转移范围，以便先进的流动性改善了流动性。通过（i）减少流动性提供商的分歧损失，（ii）减少加密和分配商人的滑倒，而（iii）（iii）提高AMM协议的流动性资本效率。据我们所知，尚无已知的方案或文献提出类似的深度学习吸引AMM，这些AMM实现了实用现实世界应用类似的资本效率和损失最小化目标。
The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
