We show that recent innovations in deep reinforcement learning can effectively color very large graphs – a well-known NP-hard problem with clear commercial applications. Because the Monte Carlo Tree Search with Upper Confidence Bound algorithm used in AlphaGoZero can improve the performance of a given heuristic, our approach allows deep neural networks trained using high performance computing (HPC) technologies to transform computation into improved heuristics with zero prior knowledge. Key to

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... ur approach is the introduction of a novel deep neural network architecture (FastColorNet) that has access to the full graph context and requires O(V) time and space to color a graph with V vertices, which enables scaling to very large graphs that arise in real applications like parallel computing, compilers, numerical solvers, and design automation, among others. As a result, we are able to learn new state of the art heuristics for graph coloring.