1 citations · 2 across the 7 of their papers we have counts for
10 papers
DAMPER: Return-Prioritized Gradient Control for Smooth Policies
Seokmin Ko, Taewon Goo, Kihyuk Hong
Actor-critic methods achieve strong performance in continuous control, but their policies can produce highly oscillatory actions. A common remedy is to add auxiliary smoothness los…
Minimax-Optimality of Posterior Sampling for Reinforcement Learning
Taewon Goo, Kihyuk Hong
Posterior sampling for reinforcement learning (PSRL) is one of the simplest and most effective exploration methods, but a basic question has remained open: does unmodified PSRL ach…
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…
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
Kihyuk Hong, Yuhang Li, Ambuj Tewari
Offline constrained reinforcement learning (RL) aims to learn a policy that maximizes the expected cumulative reward subject to constraints on expected cumulative cost using an exi…
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge
Kihyuk Hong, Yuhang Li, Ambuj Tewari
We propose an algorithm for non-stationary kernel bandits that does not require prior knowledge of the degree of non-stationarity. The algorithm follows randomized strategies obtai…