7 citations · 29 across the 18 of their papers we have counts for
4 papers · 1 filter
Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective
Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho +4
Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and one example is the proximal policy optimization algorithm with a clipped surroga…
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh +2
Whittle index policy is a powerful tool to obtain asymptotically optimal solutions for the notoriously intractable problem of restless bandits. However, finding the Whittle indices…
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu
Bayesian optimization (BO) conventionally relies on handcrafted acquisition functions (AFs) to sequentially determine the sample points. However, it has been widely observed in pra…
Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization
Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang +2
Action-constrained reinforcement learning (RL) is a widely-used approach in various real-world applications, such as scheduling in networked systems with resource constraints and c…