22 citations · 53 across the 12 of their papers we have counts for
6 papers · 1 filter
Short-Long Policy Evaluation with Novel Actions
Hyunji Alex Nam, Yash Chandak, Emma Brunskill
From incorporating LLMs in education, to identifying new drugs and improving ways to charge batteries, innovators constantly try new strategies in search of better long-term outcom…
Experiment Planning with Function Approximation
Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
We study the problem of experiment planning with function approximation in contextual bandit problems. In settings where there is a significant overhead to deploying adaptive algor…
Estimating Optimal Policy Value in General Linear Contextual Bandits
Jonathan N. Lee, Weihao Kong, Aldo Pacchiano +2
In many bandit problems, the maximal reward achievable by a policy is often unknown in advance. We consider the problem of estimating the optimal policy value in the sublinear data…
Model-based Offline Reinforcement Learning with Local Misspecification
Kefan Dong, Yannis Flet-Berliac, Allen Nie +1
We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we pro…
Offline Policy Optimization with Eligible Actions
Yao Liu, Yannis Flet-Berliac, Emma Brunskill
Offline policy optimization could have a large impact on many real-world decision-making problems, as online learning may be infeasible in many applications. Importance sampling an…
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Emma Brunskill, Bethany Leffler, Lihong Li +2
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinforc…