65 citations · 198 across the 14 of their papers we have counts for
3 papers · 1 filter
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
Andrea Zanette, Martin J. Wainwright, Emma Brunskill
Actor-critic methods are widely used in offline reinforcement learning practice, but are not so well-understood theoretically. We propose a new offline actor-critic algorithm that…
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…
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation
Andrea Zanette, Ching-An Cheng, Alekh Agarwal
Policy optimization methods are popular reinforcement learning algorithms, because their incremental and on-policy nature makes them more stable than the value-based counterparts.…