Abstrakt
Recent successes of neural networks in solving combinatorial problems and games like Go, Poker and others inspire further attempts to use deep learning approaches in discrete domains. In the field of automated planning, the most popular approach is informed forward search driven by a~heuristic function which estimates the quality of encountered states.
Designing a~powerful and easily-computable heuristics however is still a~challenging problem on many domains. In this paper, we use machine learning to construct such heuristic automatically.
We train a~neural network to predict a~minimal number of moves required to solve a~given instance of Rubik's cube. We then use the trained network as a~heuristic distance estimator with a~standard forward-search algorithm and compare the results with other heuristics.
Our experiments show that the learning approach is competitive with state-of-the-art and might be the best choice in some use-case scenarios.