Staged Design of Water Distribution Networks: A Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Staged Design of Water Distribution Networks: A Reinforcement Learning Approach

Published:2024-09-10 Issue: Volume:34 Page:111
ISSN:
Container-title:The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024)
language:
Short-container-title:

Author:

Tsiami Lydia¹²^ORCID,Makropoulos Christos¹²,Savic Dragan¹³⁴^ORCID

Affiliation:

1. KWR Water Research Institute, 3433 PE Nieuwegein, The Netherlands

2. Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, 157 80 Athens, Greece

3. Centre for Water Systems, University of Exeter, Exeter EX4 4PY, UK

4. Faculty of Civil Engineering, University of Belgrade, 11000 Belgrade, Serbia

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/111/pdf

Reference7 articles.

1. Walker, W.E., Lempert, R.J., and Kwakkel, J.H. (2013). Deep Uncertainty. Encyclopedia of Operations Research and Management Science, Springer.

2. Nagabandi, A., Clavera, I., Liu, S., Fearing, R.S., Abbeel, P., Levine, S., and Finn, C. (2018). Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning. IEEE Trans. Cogn. Dev. Syst., 1.

3. Tsiami, L., Makropoulos, C., and Savic, D.A. (2023, January 4–7). Reinforcement Learning for Adaptive Water Distribution Network Planning: Exploring its Feasibility and Potential. Proceedings of the 19th International CCWI Conference, Leicester, UK.

4. Tsiami, L., Makropoulos, C., and Savic, D. (2022, January 18–22). A review on staged design of water distribution networks. Proceedings of the 2nd International Joint WDSA/CCWI Conference; Editorial Universitat Politècnica de València, València, Spain.

5. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv.