Hyper-parameter tuned deep Q network for area estimation of oil spills: a meta-heuristic approach-Reference-Cited by-同舟云学术

Hyper-parameter tuned deep Q network for area estimation of oil spills: a meta-heuristic approach

Published:2020-10-26 Issue:1 Volume:14 Page:175-190
ISSN:1864-5909
Container-title:Evolutionary Intelligence
language:en
Short-container-title:Evol. Intel.

Author:

Banerjee Abhiit^ORCID,Ghosh Dipendranath,Das Suvrojit

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s12065-020-00500-x.pdf

Reference36 articles.

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2. Banerjee A, Ghosh D, Das S (2018) Modified firefly algorithm for area estimation and tracking of fast expanding oil spills. Appl Soft Comput 73:829–847. https://doi.org/10.1016/j.asoc.2018.09.024

3. Bellman R (1957) Dynamic programming. Dover Publications, New York

4. Brockman G, Cheung V, Pettersson L, Schneider J, Schulman J, Tang J, Zaremba W (2016) Openai gym. http://arxiv.org/abs/1606.01540. Cite arXiv:1606.01540

5. Bruemmer DJ, Dudenhoeffer DD, McKay MD, Anderson MO (2002) A robotic swarm for spill finding and perimeter formation. Tech. rep, DTIC Document

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