Title: Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero. Abstract: There have been increasing challenges to solve combinatorial optimization problems by machine learning. 
Khalil et al. (NeurIPS 2017) proposed an end-to-end reinforcement learning framework, which automatically learns graph embeddings to construct solutions to a wide range of problems.
However, it sometimes performs poorly on graphs having different characteristics than training graphs.
To improve its generalization ability to various graphs, we propose a novel learning strategy based on AlphaGo Zero, a Go engine that achieved a superhuman level without the domain knowledge of the game.
We redesign AlphaGo Zero for combinatorial optimization problems, taking into account several differences from two-player games.
In experiments on five NP-hard problems such as {\sc MinimumVertexCover} and {\sc MaxCut}, our method, with only a policy network, shows better generalization than the previous method to various instances that are not used for training, including random graphs, synthetic graphs, and real-world graphs.
Furthermore, our method is significantly enhanced by a test-time Monte Carlo Tree Search which makes full use of the policy network and value network.
We also compare recently-developed graph neural network (GNN) models, with an interesting insight into a suitable choice of GNN models for each task.