18 papers
Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning
Allen Nie, Anirudhan Badrinath, Nicholas Tomlin +5
Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can require a high level of domain exp…
A Statistical Test for the Benefits of Personalizing Interventions
Zhaoqi Li, Emma Brunskill
From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing personalization's…
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?
Stephane Hatgis-Kessell, Emma Brunskill
We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we replace classical RL algorith…
PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data
Aishwarya Mandyam, Jason Meng, Ge Gao +4
Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment. Recent advances have shown that leveraging auxiliary dataset…
Active Learning for Stochastic Contextual Linear Bandits
Emma Brunskill, Ishani Karmarkar, Zhaoqi Li
A key goal in stochastic contextual linear bandits is to efficiently learn a near-optimal policy. Prior algorithms for this problem learn a policy by strategically sampling actions…
Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs
Ashish Gurung, Ge Gao, Jordan Gutterman +6
Hybrid human-AI tutoring, where technology and humans jointly facilitate student learning, can be more beneficial than AI-only tutoring. However, preliminary evidence suggests that…