Heuristic Q-Learning Soccer Players: A New Reinforcement Learning Approach to RoboCup Simulation
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چکیده
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 Reinforcement 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 here is very promising.
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تاریخ انتشار 2007