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.

• Type: Conference
• Year: 2007
• Where: ROBOCUP
• Authors: Luiz A. Celiberto, Carlos H. C. Ribeiro, Anna Helena Reali Costa, Reinaldo A. C. Bianchi
• Keywords: Heuristics, Reinforcement Learning Algorithm, ROBOCUP 2007, Robocup 2d Simulator