Physics-informed deep reinforcement learning for enhancement on tunnel boring machine's advance speed and stability

Author:

Lin Penghui,Wu Maozhi,Xiao Zhonghua,Tiong Robert L.K.,Zhang Limao

Funder

Hebei Province Outstanding Youth Fund

Nanyang Technological University

National Natural Science Foundation of China

Huazhong University of Science and Technology

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference67 articles.

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2. M.O. Ahmed, R. Khalef, G.G. Ali, I.H. El-adaway, Evaluating deterioration of tunnels using computational machine learning algorithms, J. Constr. Eng. Manag. 147 (10) (2021) pp. 04021125. https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0002162.

3. Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU;Babaeizadeh,2016

4. A complementary learning systems approach to temporal difference learning;Blakeman;Neural Netw.,2020

5. Safe learning in robotics: from learning-based control to safe reinforcement learning;Brunke;Ann. Rev. Control, Robot. Autonomous Syst.,2022

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