1 citations · 2 across the 15 of their papers we have counts for
4 papers · 2 filters
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…
Lasso Bandit with Compatibility Condition on Optimal Arm
Harin Lee, Taehyun Hwang, Min-hwan Oh
We consider a stochastic sparse linear bandit problem where only a sparse subset of context features affects the expected reward function, i.e., the unknown reward parameter has a…
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…
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
Joongkyu Lee, Min-hwan Oh
In this paper, we study the contextual multinomial logit (MNL) bandit problem in which a learning agent sequentially selects an assortment based on contextual information, and user…