Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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SEM '05 Proceedings of the 5th international workshop on Software engineering and middleware
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KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
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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 established approach tailored to reinforcement learning following Softmax action selection policy will be shown. An application example of agent-based routing will also be illustrated.