Temporal difference learning and TD-Gammon
Communications of the ACM
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Intelligent Software Agents: Foundations and Applications
Intelligent Software Agents: Foundations and Applications
Optimizing Production Manufacturing Using Reinforcement Learning
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Engineering Applications of Artificial Intelligence
Intelligent negotiation behaviour model for an open railway access market
Expert Systems with Applications: An International Journal
Scheduling English football fixtures over the holiday period using hyper-heuristics
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Agent-based modeling and simulation of an autonomic manufacturing execution system
Computers in Industry
Computers and Operations Research
Iterative learning control based tools to learn from human error
Engineering Applications of Artificial Intelligence
Agent learning in autonomic manufacturing execution systems for enterprise networking
Computers and Industrial Engineering
Design with shape grammars and reinforcement learning
Advanced Engineering Informatics
Engineering Applications of Artificial Intelligence
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Reinforcement learning (RL) has received some attention in recent years from agent-based researchers because it deals with the problem of how an autonomous agent can learn to select proper actions for achieving its goals through interacting with its environment. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored yet. In this paper, Q-learning, a popular RL algorithm, is applied to a single machine dispatching rule selection problem. This paper investigates the application potential of Q-learning, a widely used RL algorithm to a dispatching rule selection problem on a single machine to determine if it can be used to enable a single machine agent to learn commonly accepted dispatching rules for three example cases in which the best dispatching rules have been previously defined. This study provided encouraging results that show the potential of RL for application to agent-based production scheduling.