Projects per year
Personal profile
Research Interests
Bandit Learning, Reinforcement Learning, Wireless Networks, and Bayesian Optimization
Education/Academic qualification
PhD, Electrical Engineering, Texas A&M University
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Dive into the research topics where Ping-Chun Hsieh is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Projects
- 4 Finished
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基於深度強化學習與大規模物聯網之即時無線感測(4/4)
Hsieh, P.-C. (PI)
1/09/22 → 31/08/23
Project: Government Ministry › Other Government Ministry Institute
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Deep Reinforcement Learning for Real-Time Wireless Sensing in Massive Io
Hsieh, P.-C. (PI)
1/09/21 → 30/11/22
Project: Government Ministry › Other Government Ministry Institute
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Deep Reinforcement Learning for Real-Time Wireless Sensing in Massive IoT
Hsieh, P.-C. (PI)
1/09/20 → 30/11/21
Project: Government Ministry › Other Government Ministry Institute
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Deep Reinforcement Learning for Real-Time Wireless Sensing in Massive IoT
Hsieh, P.-C. (PI)
1/09/19 → 30/11/20
Project: Government Ministry › Other Government Ministry Institute
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PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping
Huang, N. C., Hsieh, P. C., Ho, K. H. & Wu, I. C., 25 Mar 2024, Technical Tracks 14. Wooldridge, M., Dy, J. & Natarajan, S. (eds.). 11 ed. Association for the Advancement of Artificial Intelligence, p. 12600-12607 8 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 38, no. 11).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access3 Scopus citations -
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees
Su, H. E., Chen, Y. J., Hsieh, P. C. & Liu, X., 2023, In: Proceedings of Machine Learning Research. 206, p. 1331-1378 48 p.Research output: Contribution to journal › Conference article › peer-review
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Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization
Lien, Y. H., Hsieh, P. C. & Wang, Y. S., 2023, In: Proceedings of Machine Learning Research. 202, p. 20939-20949 11 p.Research output: Contribution to journal › Conference article › peer-review
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Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits: A Distributional Learning Perspective
Hung, Y. H. & Hsieh, P. C., 27 Jun 2023, AAAI-23 Technical Tracks 7. Williams, B., Chen, Y. & Neville, J. (eds.). AAAI press, p. 7944-7952 9 p. (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; vol. 37).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games
Ho, K. H., Hsieh, P. C., Lin, C. C., Luo, Y. R., Wang, F.-J. & Wu, I. C., 2023, In: Proceedings of Machine Learning Research. 222, p. 438-453 16 p.Research output: Contribution to journal › Conference article › peer-review