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Authors: Taiyi Wang 1 and Jiahao Shi 2

Affiliations: 1 Department of Computer Science, Johns Hopkins University, Baltimore, MD, U.S.A. ; 2 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, U.S.A.

Keyword(s): NP-hard, Maximal Stable Set, Deep Reinforcement Learning.

Abstract: This paper provides an innovative method to approximate the optimal solution to the maximal stable set problem, a typical NP-hard combinatorial optimization problem. Different from traditional greedy or heuristic algorithms, we combine graph embedding and DQN-based reinforcement learning to make this NP-hard optimization problem trainable so that the optimal solution over new graphs can be approximated. This appears to be a new approach in solving maximal stable set problem. The learned policy is to choose a sequence of nodes incrementally to construct the stable set, with action determined by the outputs of graph embedding network over current partial solution. Our numerical experiments suggest that the proposed algorithm is promising in tackling the maximum stable independent set problem.

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Paper citation in several formats:
Wang, T. and Shi, J. (2021). Solving Maximal Stable Set Problem via Deep Reinforcement Learning. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 483-489. DOI: 10.5220/0010179904830489

@conference{icaart21,
author={Taiyi Wang. and Jiahao Shi.},
title={Solving Maximal Stable Set Problem via Deep Reinforcement Learning},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={483-489},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010179904830489},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Solving Maximal Stable Set Problem via Deep Reinforcement Learning
SN - 978-989-758-484-8
IS - 2184-433X
AU - Wang, T.
AU - Shi, J.
PY - 2021
SP - 483
EP - 489
DO - 10.5220/0010179904830489
PB - SciTePress

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