76 citations · 125 across the 20 of their papers we have counts for
20 papers
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Jonathan N. Lee, Annie Xie, Aldo Pacchiano +4
Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trai…
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…
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan N. Lee, Alekh Agarwal, Christoph Dann +1
POMDPs capture a broad class of decision making problems, but hardness results suggest that learning is intractable even in simple settings due to the inherent partial observabilit…
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
Jonathan N. Lee, George Tucker, Ofir Nachum +2
In offline reinforcement learning (RL), a learner leverages prior logged data to learn a good policy without interacting with the environment. A major challenge in applying such me…
Model Selection in Batch Policy Optimization
Jonathan N. Lee, George Tucker, Ofir Nachum +1
We study the problem of model selection in batch policy optimization: given a fixed, partial-feedback dataset and model classes, learn a policy with performance that is competi…
Dueling RL: Reinforcement Learning with Trajectory Preferences
Aldo Pacchiano, Aadirupa Saha, Jonathan Lee
We consider the problem of preference based reinforcement learning (PbRL), where, unlike traditional reinforcement learning, an agent receives feedback only in terms of a 1 bit (0/…