Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
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 gener...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
28.05.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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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DOI: | 10.48550/arxiv.1905.11623 |