research
∙
06/15/2023
Optimal Exploration for Model-Based RL in Nonlinear Systems
Learning to control unknown nonlinear dynamical systems is a fundamental...
share
research
∙
04/24/2023
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
We consider the development of adaptive, instance-dependent algorithms f...
research
∙
11/09/2022
Leveraging Offline Data in Online Reinforcement Learning
Two central paradigms have emerged in the reinforcement learning (RL) co...
research
∙
07/06/2022
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design
While much progress has been made in understanding the minimax sample co...
research
∙
06/22/2022
Active Learning with Safety Constraints
Active learning methods have shown great promise in reducing the number ...
research
∙
01/26/2022
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Reward-free reinforcement learning (RL) considers the setting where the ...
research
∙
12/07/2021
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Obtaining first-order regret bounds – regret bounds scaling not as the w...
research
∙
11/23/2021
Best Arm Identification with Safety Constraints
The best arm identification problem in the multi-armed bandit setting is...
research
∙
08/05/2021
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning
The theory of reinforcement learning has focused on two fundamental prob...
research
∙
02/10/2021
Task-Optimal Exploration in Linear Dynamical Systems
Exploration in unknown environments is a fundamental problem in reinforc...
research
∙
11/01/2020
Experimental Design for Regret Minimization in Linear Bandits
In this paper we propose a novel experimental design-based algorithm to ...
research
∙
02/02/2020
