Heuristic reinforcement learning applied to RoboCup simulation agents
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2008-07-10
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CELIBERTO JUNIOR, L. A.
RIBEIRO, C. H. C.
COSTA A. H. R.
Reinaldo Bianchi
RIBEIRO, C. H. C.
COSTA A. H. R.
Reinaldo Bianchi
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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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CELIBERTO JUNIOR, L. A.; RIBEIRO, C. H. C.; COSTA A. H. R.;BIANCHI, R. Heuristic reinforcement learning applied to RoboCup simulation agents. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). p. 220-227, July, 2008.
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This paper describes the design and implementation of robotic agents for the RoboCup Simulation 2D category that learns using a recently proposed Heuristic Reinforcement Learning algorithm, the Heuristically Accelerated Q-Learning (HAQL). This algorithm allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q-Learning. A heuristic function that influences the choice of the actions characterizes the HAQL algorithm. A set of empirical evaluations was conducted in the RoboCup 2D Simulator, and experimental results show that even very simple heuristics enhances significantly the performance of the agents. © 2008 Springer-Verlag Berlin Heidelberg.