Rule-based Policy Regularization for Reinforcement Learning-based Building Control
Published in The 14th ACM International Conference on Future Energy Systems (ACM e-Energy 2023), 2023
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Publications
B2RL: An open-source Dataset for Building Batch Reinforcement Learning
Published in RLEM Workshop (BuildSys), 2022
Link to conference presentation video
Recommended citation: Liu, Hsin-Yu, Xiaohan Fu, Bharathan Balaji, Rajesh Gupta, and Dezhi Hong. "B2RL: an open-source dataset for building batch reinforcement learning." In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 462-465. 2022. https://arxiv.org/abs/2209.15626
Safe HVAC Control via Batch Reinforcement Learning
Published in International Conference on Cyber-Physical Systems (ICCPS), 2022
Recommended citation: Liu, H. Y., Balaji, B., Gao, S., Gupta, R., & Hong, D. (2022, May). Safe HVAC Control via Batch Reinforcement Learning. In 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) (pp. 181-192). IEEE. https://conferences.computer.org/cpsiot/pdfs/ICCPS2022-ifhdJu28kaMK8qGYbf7d0/096700a181/096700a181.pdf
Offline Reinforcement Learning with Munchausen Regularization
Published in NeurIPS 2021 Offline RL Workshop, 2021
Recommended citation: Liu, Hsin-Yu, Bharathan Balaji, Rajesh Gupta, and Dezhi Hong. " Offline Reinforcement Learning with Munchausen Regularization " Offline Reinforcement Learning Workshop at Neural Information Processing Systems, 2021 (NeurIPS 2021). http://mesl.ucsd.edu/pubs/Hsinyu_NeurIPS2021_OfflineRL.pdf
SVM Learning for GFIS Trimer Health Monitoring in Helium-Neon Ion Beam Microscopy
Published in Advanced Process Control Conference (APC), 2019
Recommended citation: Kahng, A. B., Liu, H. Y., Park, C., Pichumani, R., & Saul, L. SVM Learning for GFIS Trimer Health Monitoring in Helium-Neon Ion Beam Microscopy., Advanced Process Control Conference, 2019 https://vlsicad.ucsd.edu/Publications/Conferences/372/c372.pdf
METRICS 2.0: A machine-learning based optimization system for IC design
Published in Workshop on Open-Source EDA Technology, 2018
Recommended citation: Hashemi, S., Ho, C. T., Kahng, A. B., Liu, H. Y., & Reda, S. (2018). METRICS 2.0: A machine-learning based optimization system for IC design. In Workshop on Open-Source EDA Technology (p. 21). https://woset-workshop.github.io/PDFs/2018/a21.pdf