Learning a Partial Behavior for a Competitive Robotic Soccer Agent

Robotic soccer is a highly competitive domain. Accordingly, the use of learnt behaviors in this application field presumes not only learning algorithms that are known to converge and produce stable results, but also imposes the wish for obtaining optimal or at least near-optimal behaviors, even when working within high-dimensional and continuous state/action spaces. This paper deals with the continuous amelioration of adaptive soccer playing skills in robotic soccer simulation, documenting and presenting results of our hunt for optimal policies. We show that not too much effort is necessary to realize straightforward Reinforcement Learning algorithms in this domain, but that a heavy load of work is required when tweaking them towards competitiveness.