Abstract
In a complex environment where the messages exchange tensely among the agents, a difficulty task is to decide the best action for new arriving messages during on-line control. The Meta-Level Control model is modified and used to improve the performance of the communication among the agents in this research. During the control process, the decision is made from the experience acquired by the agents with reinforcement learning. The research proposed a Messages Meta Manager (MMM) model for Air Flow Management System (AFMS) with the combination of the Meta-Level Control approach and reinforcement learning algorithms. With the developed system, the cases of initial heuristic (IH), epsilon adaptative (EA) and performance heuristic (PH) were tested. The results from simulation and analyses show the satisfactory to the research purpose.
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Alves, D.P., Weigang, L., Souza, B.B. (2006). Using Meta-Level Control with Reinforcement Learning to Improve the Performance of the Agents. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_138
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DOI: https://doi.org/10.1007/11881599_138
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