  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
