Paper: using Deep Reinforcement Learning agents to solve the Ambulance Location Problem скачать в хорошем качестве

Paper: using Deep Reinforcement Learning agents to solve the Ambulance Location Problem

Paper: https://arxiv.org/abs/2101.04434 GitHub: https://github.com/MichaelAllen1966/q... Developing an OpenAI Gym-compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem. The simulation environment, using SimPy and OpenAI gym, provides an environment where: Incidents occurs in areas within a world with fixed dimensions. The geographic pattern of incidents may change throughout the day. When an incident occurs, ambulances are dispatched from fixed dispatch points; the closest free ambulance is used. The ambulance collects a patient and conveys them to the closest hospital. The ambulance is then allocated, by an agent, to any dispatch point. The ambulance travels to that dispatch point where it becomes available to respond to incidents (the ambulance may also be allocated while travelling to a dispatch point, depending on simulation environment settings). The job of the agent is to allocate ambulances to dispatch points in order to minimise the time from incident to arrival of ambulance at the scene of the incident. The paper (with code repository) implements a range of Deep Q Network architectures to solve this problem. Long tutorial (with code) on Q learning applied to controlling the number of staffed beds in a simple hospital simulation: The learning hospital - a basic Deep RL system. YouTube description linkls to code and paper.    • HSMA Deep Learning Tutorial. Introducing D...