6 papers
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions
Marc Brooks, Gabriel Durham, Kihyuk Hong +1
Recent advances in generative artificial intelligence (GenAI) models have enabled the generation of personalized content that adapts to up-to-date user context. While personalized…
Offline Constrained Reinforcement Learning under Partial Data Coverage
Seokmin Ko, Ambuj Tewari, Kihyuk Hong
We study offline constrained reinforcement learning with general function approximation in discounted constrained Markov decision processes. Prior methods either require full data…
A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
Kihyuk Hong, Ambuj Tewari
We study reinforcement learning in infinite-horizon average-reward settings with linear MDPs. Previous work addresses this problem by approximating the average-reward setting by di…
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
Woojin Chae, Kihyuk Hong, Yufan Zhang +2
This paper proposes a computationally tractable algorithm for learning infinite-horizon average-reward linear mixture Markov decision processes (MDPs) under the Bellman optimality…
Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs
Kihyuk Hong, Woojin Chae, Yufan Zhang +2
We study the problem of infinite-horizon average-reward reinforcement learning with linear Markov decision processes (MDPs). The associated Bellman operator of the problem not bein…
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
Kihyuk Hong, Ambuj Tewari
We study offline reinforcement learning (RL) with linear MDPs under the infinite-horizon discounted setting which aims to learn a policy that maximizes the expected discounted cumu…