Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching

2020/11/09 by Hamed Khorasgani, Haiyan Wang, Khorasgani, Hamed +3
Engineering · #Artificial Intelligence (cs.AI) #Belt Conveyor Systems Engineering #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mining Techniques and Economics #Scheduling and Optimization Algorithms

paper · pdf · doi:10.48550/arxiv.2011.05570

openalex publication_date 2020/11/09 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Continuous control with deep reinforcement learning
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Safe Exploration in Continuous Action Spaces
• Challenges of Real-World Reinforcement Learning
• Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
• Dota 2 with Large Scale Deep Reinforcement Learning
• MOPO: Model-based Offline Policy Optimization
• Model-based Adversarial Meta-Reinforcement Learning

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