# Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.10713 · HTML: https://mlsanity.com/paper/2008.10713/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

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

- [K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents](https://mlsanity.com/paper/2201.07092/) (2022)
- [DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery](https://mlsanity.com/paper/2103.03450/) (2021)
- [MCTS Based Dispatch of Autonomous Vehicles under Operational Constraints for Continuous Transportation](https://mlsanity.com/paper/2407.16200/) (2024)
- [Reinforcement Learning with Curriculum-inspired Adaptive Direct Policy Guidance for Truck Dispatching](https://mlsanity.com/paper/2502.20845/) (2025)

## Abstract (arXiv)

Dynamic dispatching is one of the core problems for operation optimization in traditional industries such as mining, as it is about how to smartly allocate the right resources to the right place at the right time. Conventionally, the industry relies on heuristics or even human intuitions which are often short-sighted and sub-optimal solutions. Leveraging the power of AI and Internet of Things (IoT), data-driven automation is reshaping this area. However, facing its own challenges such as large-scale and heterogenous trucks running in a highly dynamic environment, it can barely adopt methods developed in other domains (e.g., ride-sharing). In this paper, we propose a novel Deep Reinforcement Learning approach to solve the dynamic dispatching problem in mining. We first develop an event-based mining simulator with parameters calibrated in real mines. Then we propose an experience-sharing Deep Q Network with a novel abstract state/action representation to learn memories from heterogeneous agents altogether and realizes learning in a centralized way. We demonstrate that the proposed methods significantly outperform the most widely adopted approaches in the industry by 5.56% in terms of productivity. The proposed approach has great potential in a broader range of industries (e.g., manufacturing, logistics) which have a large-scale of heterogenous equipment working in a highly dynamic environment, as a general framework for dynamic resource allocation.