Papers › Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero

Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero

28 May 2019arXiv:1905.11623archive 2025-07-28

Kenshin Abe, Zijian Xu, Issei Sato, Masashi Sugiyama

There have been increasing challenges to solve combinatorial optimization problems by machine learning. Khalil et al. proposed an end-to-end reinforcement learning framework, S2V-DQN, which automatically learns graph embeddings to construct solutions to a wide range of problems. To improve the generalization ability of their Q-learning method, we propose a novel learning strategy based on AlphaGo Zero which is a Go engine that achieved a superhuman level without the domain knowledge of the game. Our framework is redesigned for combinatorial problems, where the final reward might take any real number instead of a binary response, win/lose. In experiments conducted for five kinds of NP-hard problems including {\sc MinimumVertexCover} and {\sc MaxCut}, our method is shown to generalize better to various graphs than S2V-DQN. Furthermore, our method can be combined with recently-developed graph neural network (GNN) models such as the \emph{Graph Isomorphism Network}, resulting in even better performance. This experiment also gives an interesting insight into a suitable choice of GNN models for each task.

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escape_ansi xuzijian629/combopt-zero/max-clique/train_controller.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 838f16a38dd69739 · report
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Tasks

Combinatorial OptimizationGraph Neural NetworkQ-LearningReinforcement Learning

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Methods

Graph Neural NetworkQ-Learning

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