# Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching

arXiv: https://arxiv.org/abs/2011.05570 · HTML: https://mlsanity.com/paper/2011.05570/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [An Offline Deep Reinforcement Learning for Maintenance Decision-Making](https://mlsanity.com/paper/2109.15050/) (2021)
- [K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents](https://mlsanity.com/paper/2201.07092/) (2022)
- [Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems](https://mlsanity.com/paper/2409.18435/) (2024)
- [A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2208.00808/) (2022)

## Abstract (arXiv)

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.