论文标题
Dyfen：支付渠道网络中的基于代理的费用设定
DyFEn: Agent-Based Fee Setting in Payment Channel Networks
论文作者
论文摘要
近年来，随着易于使用的学习环境的开发，通过利用这些框架，通过实施和可重现的增强学习算法的基准测试。在本文中，我们介绍了动态费用学习环境（DYFEN），这是一种开源现实世界的财务网络模型。它可以提供用于评估不同强化学习技术的测试台。为了说明堤防的承诺，我们提出了一个具有挑战性的问题，这是链支付渠道的同时多通道动态费用设置。这个问题在比特币闪电网络中众所周知，没有有效的解决方案。具体而言，我们报告了有关这种动态费用设定任务的几种常用的深入增强学习方法的经验结果，作为进一步实验的基准。据我们所知，这项工作提出了基于区块链和分布式分类帐技术的模拟的第一个虚拟学习环境，这与许多其他基于物理模拟或游戏平台的其他学习环境不同。
In recent years, with the development of easy to use learning environments, implementing and reproducible benchmarking of reinforcement learning algorithms has been largely accelerated by utilizing these frameworks. In this article, we introduce the Dynamic Fee learning Environment (DyFEn), an open-source real-world financial network model. It can provide a testbed for evaluating different reinforcement learning techniques. To illustrate the promise of DyFEn, we present a challenging problem which is a simultaneous multi-channel dynamic fee setting for off-chain payment channels. This problem is well-known in the Bitcoin Lightning Network and has no effective solutions. Specifically, we report the empirical results of several commonly used deep reinforcement learning methods on this dynamic fee setting task as a baseline for further experiments. To the best of our knowledge, this work proposes the first virtual learning environment based on a simulation of blockchain and distributed ledger technologies, unlike many others which are based on physics simulations or game platforms.
