6 papers
A Modularized Framework for Piecewise-Stationary Restless Bandits
Kuan-Ta Li, Chia-Chun Lin, Ping-Chun Hsieh +1
We study the piecewise-stationary restless multi-armed bandit (PS-RMAB) problem, where each arm evolves as a Markov chain but \emph{mean rewards may change across unknown segments}…
Cross-Domain Policy Optimization via Bellman Consistency and Hybrid Critics
Ming-Hong Chen, Kuan-Chen Pan, You-De Huang +2
Cross-domain reinforcement learning (CDRL) is meant to improve the data efficiency of RL by leveraging the data samples collected from a source domain to facilitate the learning in…
Non-Stationary Restless Multi-Armed Bandits with Provable Guarantee
Yu-Heng Hung, Ping-Chun Hsieh, Kai Wang
Online restless multi-armed bandits (RMABs) typically assume that each arm follows a stationary Markov Decision Process (MDP) with fixed state transitions and rewards. However, in…
BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL
Yu-Heng Hung, Kai-Jie Lin, Yu-Heng Lin +3
Bayesian optimization (BO) offers an efficient pipeline for optimizing black-box functions with the help of a Gaussian process prior and an acquisition function (AF). Recently, in…
Image Deraining via Self-supervised Reinforcement Learning
He-Hao Liao, Yan-Tsung Peng, Wen-Tao Chu +2
The quality of images captured outdoors is often affected by the weather. One factor that interferes with sight is rain, which can obstruct the view of observers and computer visio…
Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs
Yu-Heng Hung, Ping-Chun Hsieh, Akshay Mete +1
We consider the infinite-horizon linear Markov Decision Processes (MDPs), where the transition probabilities of the dynamic model can be linearly parameterized with the help of a p…