论文标题
将深度加强学习网络与卫生系统模拟集成
Integrating Deep Reinforcement Learning Networks with Health System Simulations
论文作者
论文摘要
背景和动机：结合深度强化学习（深度RL）和卫生系统模拟具有重要的潜力，可以研究改善深度RL性能和安全性以及在操作实践中。尽管存在用于深度RL和卫生系统模拟的单个工具包，但尚未建立整合两者的框架。 目的：提供一个将深入RL网络与卫生系统模拟集成的框架，并确保该框架与使用OpenAI Gym开发和测试的Deep RL代理兼容。 方法：我们基于OpenAI Gym框架开发了框架，并展示了其在简单的医院床容量模型上的用途。我们使用Pytorch建造了深RL代理，并使用Simpy构建了医院模拟。 结果：我们使用双重深Q网络或Dueling Double Deep Q网络作为Deep RL代理展示了示例模型。 结论：simpy可用于创建与在OpenAI健身环境中开发和测试的代理相兼容的卫生系统模拟。 代码的GitHub存储库：https：//github.com/michaelallen1966/learninghospital
Background and motivation: Combining Deep Reinforcement Learning (Deep RL) and Health Systems Simulations has significant potential, for both research into improving Deep RL performance and safety, and in operational practice. While individual toolkits exist for Deep RL and Health Systems Simulations, no framework to integrate the two has been established. Aim: Provide a framework for integrating Deep RL Networks with Health System Simulations, and to ensure this framework is compatible with Deep RL agents that have been developed and tested using OpenAI Gym. Methods: We developed our framework based on the OpenAI Gym framework, and demonstrate its use on a simple hospital bed capacity model. We built the Deep RL agents using PyTorch, and the Hospital Simulatation using SimPy. Results: We demonstrate example models using a Double Deep Q Network or a Duelling Double Deep Q Network as the Deep RL agent. Conclusion: SimPy may be used to create Health System Simulations that are compatible with agents developed and tested on OpenAI Gym environments. GitHub repository of code: https://github.com/MichaelAllen1966/learninghospital
