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Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent
Joongkyu Lee, Min-hwan Oh
We study nonstationary generalized linear bandits (GLBs), where the expected reward is modeled through a nonlinear link function with an unknown time-varying parameter. This framew…
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
Joongkyu Lee, Min-hwan Oh
We study optimal experimental design for multinomial logit (MNL) bandits, where an agent repeatedly selects a subset of items from a ground set of size and observes single-…
Combinatorial Reinforcement Learning with Preference Feedback
Joongkyu Lee, Min-hwan Oh
In this paper, we consider combinatorial reinforcement learning with preference feedback, where a learning agent sequentially offers an action--an assortment of multiple items to--…
Improved Online Confidence Bounds for Multinomial Logistic Bandits
Joongkyu Lee, Min-hwan Oh
In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regre…
Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
Joongkyu Lee, Min-hwan Oh
In this work, we prove that, in linear MDPs, the feature dimension is lower bounded by in order to aptly represent transition probabilities, where is the size of the…
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
Wooseong Cho, Taehyun Hwang, Joongkyu Lee +1
We study reinforcement learning with multinomial logistic (MNL) function approximation where the underlying transition probability kernel of the Markov decision processes (MDPs) is…