Model-Based Reinforcement Learning for Evolving Soccer Strategies
Marco Wiering, Rafał Sałustowicz, Jürgen Schmidhuber · Studies in fuzziness and soft computing · 2001
We use reinforcement learning (RL) to evolve soccer team strategies. RL may profit significantly from world models (WMs). In high-dimensional, continuous input spaces, however, learning accurate WMs is intractable. In this chapter, we show that incomplete WMs can help to quickly find good policies. Our approach is based on a novel combination of CMACs and prioritized sweeping. Variants thereof outperform other algorithms used in previous work. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.