论文标题
在动态调度中应用深入的强化学习的挑战
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
论文作者
论文摘要
动态调度旨在在正确的时间将正确的资源巧妙地分配到正确的位置。动态调度是采矿行业运营优化的核心问题之一。从理论上讲，深度加固学习（RL）应该是自然而然的解决这个问题的。但是，该行业依靠启发式方法甚至人类直觉，这些直觉通常是短视和最佳的解决方案。在本文中，我们回顾了使用Deep RL来解决采矿业动态调度问题的主要挑战。
Dynamic dispatching aims to smartly allocate the right resources to the right place at the right time. Dynamic dispatching is one of the core problems for operations optimization in the mining industry. Theoretically, deep reinforcement learning (RL) should be a natural fit to solve this problem. However, the industry relies on heuristics or even human intuitions, which are often short-sighted and sub-optimal solutions. In this paper, we review the main challenges in using deep RL to address the dynamic dispatching problem in the mining industry.
