Heuristically accelerated Q-learning: A new approach to speed up reinforcement learning

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2004-01-05
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Reinaldo Bianchi
RIBEIRO, C. H. C.
COSTA, A. H. R.
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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BIANCHI, R.; RIBEIRO, C. H. C.; COSTA, A. H. R. Heuristically accelerated Q-learning: A new approach to speed up reinforcement learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 3171, p. 245-254, 2004.
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This work presents a new algorithm, called Heuristically Accelerated Q-Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q-learning. A heuristic function H that influences the choice of the actions characterizes the HAQL algorithm. The heuristic function is strongly associated with the policy: it indicates that an action must be taken instead of another. This work also proposes an automatic method for the extraction of the heuristic function H from the learning process, called Heuristic from Exploration. Finally, experimental results shows that even a very simple heuristic results in a significant enhancement of performance of the reinforcement learning algorithm. © Springer-Verlag 2004.

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