3 citations · 9 across the 6 of their papers we have counts for
8 papers · 1 filter
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…
Design of Experiments for Stochastic Contextual Linear Bandits
Andrea Zanette, Kefan Dong, Jonathan Lee +1
In the stochastic linear contextual bandit setting there exist several minimax procedures for exploration with policies that are reactive to the data being acquired. In practice, t…
Near Optimal Policy Optimization via REPS
Aldo Pacchiano, Jonathan Lee, Peter Bartlett +1
Since its introduction a decade ago, \emph{relative entropy policy search} (REPS) has demonstrated successful policy learning on a number of simulated and real-world robotic domain…
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…
Is Q-Learning Provably Efficient? An Extended Analysis
Kushagra Rastogi, Jonathan Lee, Fabrice Harel-Canada +1
This work extends the analysis of the theoretical results presented within the paper Is Q-Learning Provably Efficient? by Jin et al. We include a survey of related research to cont…
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan N. Lee, Aldo Pacchiano, Peter Bartlett +1
Maximum a posteriori (MAP) inference in discrete-valued Markov random fields is a fundamental problem in machine learning that involves identifying the most likely configuration of…