论文标题
基于代理的快速仿真框架，以及用于加强学习的应用程序和交易潜伏期效应的研究
Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects
论文作者
论文摘要
我们引入了一个新的软件工具箱，用于基于代理的仿真。通过提供用户友好的Python API来促进快速原型，其核心基于有效的C ++实现，以支持大规模多代理系统的模拟。我们的软件环境受益于多功能消息驱动的体系结构。它最初是为了支持金融市场的研究而开发的，它提供了模拟不同（易于自定义）市场规则的广泛范围的灵活性，并研究辅助因素（例如延迟）对市场动态的影响。作为一个简单的说明，我们使用工具箱来研究订单处理延迟在正常交易中的作用以及价格重大变化的情况。由于其一般体系结构，我们的工具箱也可以用作通用多代理系统模拟器。我们通过模拟了先前在文献中提出的多机构网络路由方案中未重新学习试剂协调的机制，提供了这种非财务应用的示例。
We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementation to support simulation of large-scale multi-agent systems. Our software environment benefits from a versatile message-driven architecture. Originally developed to support research on financial markets, it offers the flexibility to simulate a wide-range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics. As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change. Owing to its general architecture, our toolbox can also be employed as a generic multi-agent system simulator. We provide an example of such a non-financial application by simulating a mechanism for the coordination of no-regret learning agents in a multi-agent network routing scenario previously proposed in the literature.
