Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching

• URL: http://arxiv.org/abs/2011.05570v1
• Date: Mon, 9 Nov 2020 22:26:45 GMT
• Title: Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
• Authors: Hamed Khorasgani, Haiyan Wang, Chetan Gupta
• Abstract summary: Dynamic dispatching is one of the core problems for operations optimization in the mining industry. Deep reinforcement learning should be a natural fit to solve this problem. In this paper, we review the main challenges in using deep RL to address the dynamic dispatching problem.
• Score: 14.412373060545491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• ODRL: A Benchmark for Off-Dynamics Reinforcement Learning [59.72217833812439]
  We introduce ODRL, the first benchmark tailored for evaluating off-dynamics RL methods. ODRL contains four experimental settings where the source and target domains can be either online or offline. We conduct extensive benchmarking experiments, which show that no method has universal advantages across varied dynamics shifts.
  arXiv  Detail & Related papers  (2024-10-28T05:29:38Z)
• Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations [0.6116681488656472]
  This study addresses the dynamic order picking problem. Traditional methods, often assuming fixed order sets, fall short in this dynamic environment. We utilize Deep Reinforcement Learning (DRL) as a solution methodology to handle the inherent uncertainties in customer demands.
  arXiv  Detail & Related papers  (2024-08-03T03:56:46Z)
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• Intrinsically-Motivated Reinforcement Learning: A Brief Introduction [0.0]
  Reinforcement learning (RL) is one of the three basic paradigms of machine learning. In this paper, we investigated the problem of improving exploration in RL and introduced the intrinsically-motivated RL.
  arXiv  Detail & Related papers  (2022-03-03T12:39:58Z)
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives [92.0321404272942]
  Reinforcement learning can be used to build general-purpose robotic systems. However, training RL agents to solve robotics tasks still remains challenging. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
  arXiv  Detail & Related papers  (2021-10-28T17:59:30Z)
• Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems [4.692304496312442]
  This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP) In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision.
  arXiv  Detail & Related papers  (2021-02-11T07:25:04Z)
• Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning [10.835960004409708]
  We propose a novel Deep Reinforcement Learning approach to solve the dynamic 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.
  arXiv  Detail & Related papers  (2020-08-24T21:29:56Z)
• Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning [90.93035276307239]
  We propose an information theoretic regularization objective and an annealing-based optimization method to achieve better generalization ability in RL agents. We demonstrate the extreme generalization benefits of our approach in different domains ranging from maze navigation to robotic tasks. This work provides a principled way to improve generalization in RL by gradually removing information that is redundant for task-solving.
  arXiv  Detail & Related papers  (2020-08-03T02:24:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.