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ROBOCUP
2007
Springer

Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents

10 years 7 months ago
Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents
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.
Luiz A. Celiberto, Carlos H. C. Ribeiro, Anna Hele
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where ROBOCUP
Authors Luiz A. Celiberto, Carlos H. C. Ribeiro, Anna Helena Reali Costa, Reinaldo A. C. Bianchi
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