7 citations · 29 across the 18 of their papers we have counts for
13 papers · 1 filter
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective
Yu-Wei Yang, Yun-Ming Chan, Wei Hung +2
Offline model-based reinforcement learning (MBRL) serves as a competitive framework that can learn well-performing policies solely from pre-collected data with the help of learned…
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees
Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh +1
We revisit the domain of off-policy policy optimization in RL from the perspective of coordinate ascent. One commonly-used approach is to leverage the off-policy policy gradient to…
Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
Wei Hung, Bo-Kai Huang, Ping-Chun Hsieh +1
Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learnin…
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits
Yu-Heng Hung, Ping-Chun Hsieh
Reward-biased maximum likelihood estimation (RBMLE) is a classic principle in the adaptive control literature for tackling explore-exploit trade-offs. This paper studies the stocha…
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…
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
Yu-Heng Hung, Ping-Chun Hsieh, Xi Liu +1
Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit tra…
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
Xi Liu, Li Li, Ping-Chun Hsieh +3
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experie…
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
Xi Liu, Ping-Chun Hsieh, Anirban Bhattacharya +1
Inspired by the Reward-Biased Maximum Likelihood Estimate method of adaptive control, we propose RBMLE -- a novel family of learning algorithms for stochastic multi-armed bandits (…
Streaming Network Embedding through Local Actions
Xi Liu, Ping-Chun Hsieh, Nick Duffield +3
Recently, considerable research attention has been paid to network embedding, a popular approach to construct feature vectors of vertices. Due to the curse of dimensionality and sp…
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya +1
Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection. In these applications…