Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Natural Science Foundation of Chongqing
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Sun, J.; Qi, G.; Mazur, N.; Zhu, Z.: Structural scheduling of transient control under energy storage systems by sparse-promoting reinforcement learning. IEEE Trans. Ind. Inf. 18(2), 744–756 (2022)
2. Sun, J.; Li, P.; Wang, C.: Optimise transient control against dos attacks on ESS by input convex neural networks in a game. Sustain. Energy Grids Netw. 28, 100535 (2021)
3. Mannucci, T.; Kampen, E.J.V.; Visser, C.D.; Chu, Q.: Safe exploration algorithms for reinforcement learning controllers. IEEE Trans. Neural Netw. Learn. Syst. 29, 1069–1081 (2018)
4. Brunke, L.; Greeff, M.; Hall, A.W.; Yuan, Z.; Zhou, S.; Panerati, J.; Schoellig, A.P.: Safe learning in robotics: from learning-based control to safe reinforcement learning. Ann. Rev. Control Robot. Auton. Syst. 5, 5 (2022)
5. Thananjeyan, B.; Balakrishna, A.; Nair, S.; Luo, M.; Srinivasan, K.; Hwang, M.; Gonzalez, J.E.; Ibarz, J.; Finn, C.; Goldberg, K.: Recovery RL: safe reinforcement learning with learned recovery zones. IEEE Robot. Autom. Lett. 6, 4915–4922 (2021)