Heuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation
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2007-12-03
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CELIBERTO JUNIOR, L. A.
MATSMURA, J.
Reinaldo Bianchi
MATSMURA, J.
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.; MATSMURA, J.; BIANCHI, R. Heuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 520-529, Dec. 2007.
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This paper describes the design and implementation of a 4 player RoboCup Simulation 2D team, which was build by adding Heuristic: Accelerated Reinforcement Learning capabilities to basic players of the well-known UvA Trilearn team. The implemented agents learn by using a recently proposed Heuristic Heinforcement Learning algorithm, the Heuristically Accelerated Q-Learning (HAQL), which allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q-Learning. A set of empirical evaluations was conducted in the RoboCup 2D Simulator, and experimental results obtained while playing with other teams shows that the approach adopted hero is very promising. © Springer-Verlag Berlin Heidelberg 2007.