Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control-Reference-Cited by-同舟云学术

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Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control

Published:2024-07 Issue:7 Volume:150 Page:
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Tabas Sadegh Sadeghi¹,Samadi Vidya²^ORCID

Affiliation:

1. Ph.D. Student, School of Computing, Clemson Univ., Clemson, SC 29634; Glenn Dept. of Civil Engineering, Clemson Univ., Clemson, SC 29634.

2. Assistant Professor, Dept. of Agricultural Sciences, Clemson Univ., Clemson, SC 29634 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JWRMD5.WRENG-6089

Reference75 articles.

1. Achiam J. 2018. “Spinning up in deep reinforcement learning.” Accessed January 15 2020. https://spinningupopenaicom.

2. Performance evaluation of a water resources system under varying climatic conditions: Reliability, Resilience, Vulnerability and beyond

3. Leveraging Deep Reinforcement Learning for Water Distribution Systems with Large Action Spaces and Uncertainties: DRL-EPANET for Pressure Control

4. Bellman, R. 1957. Dynamic programming. Princeton, NJ: Princeton University Press.

5. Bellman, R. E., and S. E. Dreyfus. 2015. Applied dynamic programming. Princeton, NJ: Princeton University Press.

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