45 citations · 215 across the 20 of their papers we have counts for
8 papers · 1 filter
Online Model Selection for Reinforcement Learning with Function Approximation
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar +2
Deep reinforcement learning has achieved impressive successes yet often requires a very large amount of interaction data. This result is perhaps unsurprising, as using complicated…
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer +1
There has been growing progress on theoretical analyses for provably efficient learning in MDPs with linear function approximation, but much of the existing work has made strong as…
Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal +1
Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks. Doing batch RL in a way that yields a reliable new policy in large domains is chall…
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri +4
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables f…
Value Driven Representation for Human-in-the-Loop Reinforcement Learning
Ramtin Keramati, Emma Brunskill
Interactive adaptive systems powered by Reinforcement Learning (RL) have many potential applications, such as intelligent tutoring systems. In such systems there is typically an ex…
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky +1
When observed decisions depend only on observed features, off-policy policy evaluation (OPE) methods for sequential decision making problems can estimate the performance of evaluat…