Learning heuristics for A*
This work addresses path finding efficiency for applications in computer science, but it is incremental as it builds on existing neural algorithmic reasoning methods.
The paper tackled the problem of path finding in graphs by learning heuristic functions for the A* algorithm using neural algorithmic reasoning, resulting in A* with learned heuristics greatly speeding up target node searching compared to Dijkstra while still finding minimal-cost paths.
Path finding in graphs is one of the most studied classes of problems in computer science. In this context, search algorithms are often extended with heuristics for a more efficient search of target nodes. In this work we combine recent advancements in Neural Algorithmic Reasoning to learn efficient heuristic functions for path finding problems on graphs. At training time, we exploit multi-task learning to learn jointly the Dijkstra's algorithm and a consistent heuristic function for the A* search algorithm. At inference time, we plug our learnt heuristics into the A* algorithm. Results show that running A* over the learnt heuristics value can greatly speed up target node searching compared to Dijkstra, while still finding minimal-cost paths.