Learning heuristics for weighted CSPs through deep reinforcement learning-Reference-Cited by-同舟云学术

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Learning heuristics for weighted CSPs through deep reinforcement learning

Published:2022-08-03 Issue:8 Volume:53 Page:8844-8863
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Chen Dingding,Chen Ziyu,He Zhongshi,Gao Junsong,Su Zhizhuo

Funder

Fundamental Research Funds for the Central Universities

Graduate Research and Innovation Projects of Chongqing

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03992-5.pdf

Reference63 articles.

1. Allouche D, Givry Sd, Katsirelos G, Schiex T, Zytnicki M (2015) Anytime hybrid best-first search with tree decomposition for weighted CSP. In: CP, pp 12–29. Springer

2. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286 (5439):509–512

3. Bellman R (1957) A markovian decision process. Journal of Mathematics and Mechanics, 679–684

4. Bengio Y, Lodi A, Prouvost A (2021) Machine learning for combinatorial optimization: a methodological tour d’horizon. Eur J Oper Res 290(2):405–421

5. Cappart Q, Moisan T, Rousseau LM, Prémont-Schwarz I, Cire AA (2021) Combining reinforcement learning and constraint programming for combinatorial optimization. In: AAAI, vol 35, pp 3677–3687

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