Traditionally, permutation puzzles such as the Rubik's Cube were often solved by heuristic search like $A^*\!$-search and value based reinforcement learning methods.
Both heuristic search and Q-learning approaches to solving these puzzles can be reduced to learning a heuristic/value function to decide what puzzle move to make at each step.
We propose learning a value function using the irreducible representations basis (which we will also call the Fourier basis) of the puzzle’s underlying group.
Classical Fourier analysis on real valued functions tells us we can approximate smooth functions with low frequency basis functions. Similarly, smooth functions on finite groups can be represented by the analogous low frequency Fourier basis functions.
We demonstrate the effectiveness of learning a value function in the Fourier basis for solving various permutation puzzles and show that it outperforms standard deep learning methods.