A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer (2016)
David L. Leottau, Javier Ruiz-del-Solar, Patrick MacAlpine, and Peter Stone
Hierarchical task decomposition strategies allow robots and agents in general to address complex decision-making tasks. Layered learning is a hierarchical machine learning paradigm where a complex behavior is learned from a series of incrementally trained sub-tasks. This paper describes how layered learning can be applied to design individual behaviors in the context of soccer robotics. Three different layered learning strategies are implemented and analyzed using a ball-dribbling behavior as a case study. Performance indices for evaluating dribbling speed and ball-control are defined and measured. Experimental results validate the usefulness of the implemented layered learning strategies showing a trade-off between performance and learning speed.
In {R}obo{C}up-2015: Robot Soccer World Cup {XIX}, Luis Almeida and Jianmin Ji and Gerald Steinbauer and Sean Luke (Eds.), Berlin, Germany 2016. Springer Verlag.

Patrick MacAlpine Ph.D. Student patmac [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu