5 citations · 29 across the 29 of their papers we have counts for
4 papers · 1 filter
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Andrew Wagenmaker, Yifang Chen, Max Simchowitz +2
Obtaining first-order regret bounds -- regret bounds scaling not as the worst-case but with some measure of the performance of the optimal policy on a given instance -- is a core q…
Best Arm Identification with Safety Constraints
Zhenlin Wang, Andrew Wagenmaker, Kevin Jamieson
The best arm identification problem in the multi-armed bandit setting is an excellent model of many real-world decision-making problems, yet it fails to capture the fact that in th…
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning
Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson
The theory of reinforcement learning has focused on two fundamental problems: achieving low regret, and identifying -optimal policies. While a simple reduction allows one to app…
Task-Optimal Exploration in Linear Dynamical Systems
Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson
Exploration in unknown environments is a fundamental problem in reinforcement learning and control. In this work, we study task-guided exploration and determine what precisely an a…