发表机构
University of Liverpool; King’s College London; University of Oxford（利物浦大学; 伦敦国王学院; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SAiFE_gym，一个用于集中流动性自动做市的模拟环境模块，通过向量化优化支持高维强化学习，并在市场参数不确定下验证了RL代理的性能。
AI 中文摘要
我们提出了SAiFE_gym，这是一个Python模块，提供了一系列模拟环境，用于研究具有集中流动性（CL）的恒定产品市场（CPMs）中的交易问题。这些市场赋予流动性提供者（LPs）对其资本分配方式的精细控制，并使他们能够根据市场条件动态调整其流动性提供范围，这反过来又决定了他们如何赚取费用。我们将具有CL的CPMs的微观结构分解为交互式组件，使研究人员和从业者能够捕捉各种经济设置。我们采用向量化方法来优化我们的环境，使其可扩展以适应高维强化学习（RL）工作流，这些工作流最能描述顺序决策问题。我们通过评估RL代理在具有CL的CPMs中在市场参数不确定性下的性能，展示了我们环境的优势。
英文摘要
We present SAiFE_gym, a Python module that provides a collection of simulation environments for studying trading problems in Constant Product Markets (CPMs) with Concentrated Liquidity (CL). These markets give Liquidity Providers (LPs) granular control over how their capital is allocated and enable them to adjust their range of liquidity provision dynamically based on market conditions, which in turn, dictates how they earn fees. We decompose the microstructure of CPMs with CL in interactive components that allow researchers and practitioners to capture various economic settings. We employ a vectorized approach to optimize our environments, making them scalable for high dimensional Reinforcement Learning (RL) workflows that best describe sequential decision problems. We demonstrate the benefits of our environments by evaluating the performance of RL agents in CPMs with CL under uncertainty in market parameters.
Comments8 pages
