Exploring Boosted Neural Nets for Rubik s Cube Solving
We explore whether neural nets can learn good actions in the Rubik's Cube setting when given minimal domain knowledge. In doing so, we experiment with integrating neural nets with AdaBoost to dynamically weight important instances from the data set at training time. Inspired by FilterBoost, we develop a variant of AdaBoost that is better suited to problem settings with a sampling oracle, while making fewer calls to that oracle. We then test that variant using neural nets as a weak learner. Experiments demonstrate that neural nets can acheive some success at Rubik's Cube solving with minimal domain knowledge, but our variant of AdaBoost does not work well with the current trend towards larger, deeper neural nets.