Author: Thomas GabelPublisher:
ISBN:
Category :
Languages : en
Pages : 161
Book Description
Multi-agent Reinforcement Learning Approaches for Distributed Job Shop Scheduling Problems
Author: Thomas GabelLearning in Cooperative Multi-Agent Systems
Author: Thomas GabelPublisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
ISBN: 9783838110363
Category :
Languages : de
Pages : 192
Book Description
In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Job-shop scheduling represents one such application. Multi-agent reinforcement learning (RL) methods allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. However, the decentralization of the control and observation of the system among independent agents has a significant impact on problem complexity. The author Thomas Gabel addresses the intricacy of learning and acting in multi-agent systems by two complementary approaches. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. Moreover, he presents various novel model-free multi-agent RL algorithms that are capable of quickly obtaining approximate solutions in the vicinity of the optimum. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems.
Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
Author: Schirin BärPublisher: Springer Nature
ISBN: 3658391790
Category : Computers
Languages : en
Pages : 163
Book Description
The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.
A Cooperative Hierarchical Deep Reinforcement Learning Based Multi-Agent Method for Distributed Job Shop Scheduling Problem with Random Job Arrivals
Author: Jiang-Ping HuangPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Distributed manufacturing has been an important trend in the industrial field, in which the production cost can be reduced through the cooperation among factories. In the real production, the random job arrivals are regular for the enterprises with daily delivered production tasks. In the paper, Distributed Job-shop Scheduling Problem (DJSP) with random job arrivals is studied. The distributed characteristics and the uncertain disturbance raise higher demands on the responsiveness and the self-adaptiveness of the scheduling method. To meet the scheduling requirements, a hierarchical Deep Reinforcement Learning (DRL) based multi-agent method Agentin is presented where the assigning agent (Agenta) and the sequencing agent (Agents) are respectively designed for job allocation and job sequencing, and they share the system information and extract the features they need independently. Agenta and Agents are both based on the specially-designed DQN framework, which has a variable threshold probability in the training stage, and it can balance the exploitation and exploration in the model training. For Agenta and Agents, two Markov Decision Process (MDP) formulations are established with elaborately-explored state features, rules-based action spaces and objective-oriented reward functions. Based on 1350 different production instances, the independent utility tests prove the effectiveness of the independent agents and the importance of the cooperation among the agents. The comparison test with the related algorithms validates the effectiveness of the integrated multi-agent method.
Optimization and Learning
Author: Bernabé DorronsoroPublisher: Springer Nature
ISBN: 3030419134
Category : Computers
Languages : en
Pages : 298
Book Description
This volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020. The 23 full papers were carefully reviewed and selected from 55 submissions. The papers presented in the volume focus on the future challenges of optimization and learning methods, identifying and exploiting their synergies,and analyzing their applications in different fields, such as health, industry 4.0, games, logistics, etc.
Multi-Agent Coordination
Author: Arup Kumar SadhuPublisher: John Wiley & Sons
ISBN: 1119699029
Category : Computers
Languages : en
Pages : 320
Book Description
Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.
Multiagent Scheduling
Author: Alessandro AgnetisPublisher: Springer Science & Business Media
ISBN: 3642418805
Category : Business & Economics
Languages : en
Pages : 281
Book Description
Scheduling theory has received a growing interest since its origins in the second half of the 20th century. Developed initially for the study of scheduling problems with a single objective, the theory has been recently extended to problems involving multiple criteria. However, this extension has still left a gap between the classical multi-criteria approaches and some real-life problems in which not all jobs contribute to the evaluation of each criterion. In this book, we close this gap by presenting and developing multi-agent scheduling models in which subsets of jobs sharing the same resources are evaluated by different criteria. Several scenarios are introduced, depending on the definition and the intersection structure of the job subsets. Complexity results, approximation schemes, heuristics and exact algorithms are discussed for single-machine and parallel-machine scheduling environments. Definitions and algorithms are illustrated with the help of examples and figures.
Intelligent Quality Systems
Author: Duc T. PhamPublisher: Springer Science & Business Media
ISBN: 1447114981
Category : Technology & Engineering
Languages : en
Pages : 212
Book Description
Although the tenn quality does not have a precise and universally accepted definition, its meaning is generally well understood: quality is what makes the difference between success and failure in a competitive world. Given the importance of quality, there is a need for effective quality systems to ensure that the highest quality is achieved within given constraints on human, material or financial resources. This book discusses Intelligent Quality Systems, that is quality systems employing techniques from the field of Artificial Intelligence (AI). The book focuses on two popular AI techniques, expert or knowledge-based systems and neural networks. Expert systems encapsulate human expertise for solving difficult problems. Neural networks have the ability to learn problem solving from examples. The aim of the book is to illustrate applications of these techniques to the design and operation of effective quality systems. The book comprises 8 chapters. Chapter 1 provides an introduction to quality control and a general discussion of possible AI-based quality systems. Chapter 2 gives technical information on the key AI techniques of expert systems and neural networks. The use of these techniques, singly and in a combined hybrid fonn, to realise intelligent Statistical Process Control (SPC) systems for quality improvement is the subject of Chapters 3-5. Chapter 6 covers experimental design and the Taguchi method which is an effective technique for designing quality into a product or process. The application of expert systems and neural networks to facilitate experimental design is described in this chapter.
Holonic and Multi-Agent Systems for Manufacturing
Author: Vladimir MarikPublisher: Springer Science & Business Media
ISBN: 3540744789
Category : Science
Languages : en
Pages : 470
Book Description
This volume constitutes the refereed proceedings of the Third International Conference on Industrial Applications of Holonic and Multi-Agent Systems held in September 2007. The 39 full papers were selected from among 63 submissions. They are organized into topical sections covering theoretical and methodological issues, algorithms and technologies, implementation and validation, applications, and supply chain management.
Agent-Based Optimization
Author: Ireneusz CzarnowskiPublisher: Springer
ISBN: 3642340970
Category : Technology & Engineering
Languages : en
Pages : 208
Book Description
This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.