论文标题
部分可观测时空混沌系统的无模型预测
Simulating Coverage Path Planning with Roomba
论文作者
论文摘要
覆盖道路计划涉及在有障碍的环境中访问每个空缺状态。在本文中，我们在代理商最初未知的环境中探讨了这个问题，以模拟真空清洁机器人的任务。对先前工作的调查表明，在应用学习以解决此问题方面的工作很少。在本文中，我们探索了使用深入的增强学习对盖路径计划问题进行建模，并将其与室内内置算法的性能进行比较，这是一种流行的真空清洁机器人。
Coverage Path Planning involves visiting every unoccupied state in an environment with obstacles. In this paper, we explore this problem in environments which are initially unknown to the agent, for purposes of simulating the task of a vacuum cleaning robot. A survey of prior work reveals sparse effort in applying learning to solve this problem. In this paper, we explore modeling a Cover Path Planning problem using Deep Reinforcement Learning, and compare it with the performance of the built-in algorithm of the Roomba, a popular vacuum cleaning robot.
