Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
arXiv:2011.05570
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.
arXiv admin note: text overlap with arXiv:2008.10713
References in corpus (10)
- Continuous control with deep reinforcement learning
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
- Dota 2 with Large Scale Deep Reinforcement Learning
- Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
- Continuous Deep Q-Learning with Model-based Acceleration
- Safe Exploration in Continuous Action Spaces
- Challenges of Real-World Reinforcement Learning
- MOPO: Model-based Offline Policy Optimization
- Deep Robust Kalman Filter
- Model-based Adversarial Meta-Reinforcement Learning