3 citations · 7 across the 5 of their papers we have counts for
5 papers
Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
Yu-Ting Lee, Samuel Yen-Chi Chen, Fu-Chieh Chang
While parameterized quantum computations have shown success in standard reinforcement learning (RL), whether these advantages adapt to hierarchical RL (HRL) remains a critical open…
Sample Complexity of Kernel-Based Q-Learning
Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh +3
Modern reinforcement learning (RL) often faces an enormous state-action space. Existing analytical results are typically for settings with a small number of state-actions, or simpl…
Flexible Multiple-Objective Reinforcement Learning for Chip Placement
Fu-Chieh Chang, Yu-Wei Tseng, Ya-Wen Yu +11
Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the…
G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data
Fu-Chieh Chang, Hao-Jen Wang, Chun-Nan Chou +1
Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may…
Representation Learning on Large and Small Data
Chun-Nan Chou, Chuen-Kai Shie, Fu-Chieh Chang +2
Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation. This book chapter presented the im…