451 citations · 1.7k across the 73 of their papers we have counts for
5 papers · 1 filter
Provably Efficient Reinforcement Learning with Aggregated States
Shi Dong, Benjamin Van Roy, Zhengyuan Zhou
We establish that an optimistic variant of Q-learning applied to a fixed-horizon episodic Markov decision process with an aggregated state representation incurs regret $\tilde{\mat…
Information-Theoretic Confidence Bounds for Reinforcement Learning
Xiuyuan Lu, Benjamin Van Roy
We integrate information-theoretic concepts into the design and analysis of optimistic algorithms and Thompson sampling. By making a connection between information-theoretic quanti…
Comments on the Du-Kakade-Wang-Yang Lower Bounds
Benjamin Van Roy, Shi Dong
Du, Kakade, Wang, and Yang recently established intriguing lower bounds on sample complexity, which suggest that reinforcement learning with a misspecified representation is intrac…
Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel +11
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabiliti…
On the Performance of Thompson Sampling on Logistic Bandits
Shi Dong, Tengyu Ma, Benjamin Van Roy
We study the logistic bandit, in which rewards are binary with success probability and actions and coefficients are within the …