The document discusses using a self-organizing map (SOM) to discretize continuous sensor states for reinforcement learning with a Lego Mindstorms NXT robot. Experiments apply Q-learning with different state representations - with and without the SOM. The SOM helped induce smoother representations of sonar values and bump sensors, leading to better robot behavior in some cases compared to simple quantization of sensor values.