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Using generative adversarial networks to evaluate robustness of reinforcement learning agents against uncertainties
Khayatian, F., Nagy, Z., & Bollinger, A. (2021). Using generative adversarial networks to evaluate robustness of reinforcement learning agents against uncertainties. Energy and Buildings, 251, 111334 (13 pp.). https://doi.org/10.1016/j.enbuild.2021.111334
Applications of reinforcement learning in energy systems
Perera, A. T. D., & Kamalaruban, P. (2021). Applications of reinforcement learning in energy systems. Renewable and Sustainable Energy Reviews, 137, 110618 (22 pp.). https://doi.org/10.1016/j.rser.2020.110618
Adaptive laser welding control: a reinforcement learning approach
Masinelli, G., Le-Quang, T., Zanoli, S., Wasmer, K., & Shevchik, S. A. (2020). Adaptive laser welding control: a reinforcement learning approach. IEEE Access, 8, 103803-103814. https://doi.org/10.1109/ACCESS.2020.2998052
Introducing reinforcement learning to the energy system design process
Perera, A. T. D., Wickramasinghe, P. U., Nik, V. M., & Scartezzini, J. L. (2020). Introducing reinforcement learning to the energy system design process. Applied Energy, 262, 114580 (14 pp.). https://doi.org/10.1016/j.apenergy.2020.114580
In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach
Wasmer, K., Le-Quang, T., Meylan, B., & Shevchik, S. A. (2019). In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach. Journal of Materials Engineering and Performance, 28(2), 666-672. https://doi.org/10.1007/s11665-018-3690-2