This paper studies model-based reinforcement learning (RL) for regret minimization. We
focus on finite-horizon episodic RL where the transition model $ P $ belongs to a known …

We study the exploration problem with approximate linear action-value functions in episodic
reinforcement learning under the notion of low inherent Bellman error, a condition normally …

The construction in the recent paper by Du et al.[2019] implies that searching for a near-
optimal action in a bandit sometimes requires examining essentially all the actions, even if …

Value function approximation has demonstrated phenomenal empirical success in
reinforcement learning (RL). Nevertheless, despite a handful of recent progress on …

Several practical applications of reinforcement learning involve an agent learning from past
data without the possibility of further exploration. Often these applications require us to 1) …

In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn)
policies in scenarios where the data are collected from a distribution that substantially differs …

Reinforcement learning (RL) applies to control problems with large state and action spaces,
hence it is natural to consider RL with a parametric model. In this paper we focus on finite …

Value function approximation has demonstrated phenomenal empirical success in
reinforcement learning (RL). Nevertheless, despite a handful of recent progress on …

Discretization-based approaches to solving online reinforcement learning problems are
studied extensively on applications such as resource allocation and cache management …

Low-complexity models such as linear function representation play a pivotal role in enabling
sample-efficient reinforcement learning (RL). The current paper pertains to a scenario with …