Abstract
In this paper a combined use of reinforcement learning and simulated annealing is treated. Most of the simulated annealing methods suggest using heuristic temperature bounds as the basis of annealing. Here a theoretically es- tablished approach tailored to reinforcement learning following Softmax action selection policy will be shown. An application example of agent-based routing will also be illustrated.
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Stefán, P., Monostori, L. (2001). On the Relationship between Learning Capability and the Boltzmann-Formula. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_26
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DOI: https://doi.org/10.1007/3-540-45517-5_26
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