14 citations · 21 across the 3 of their papers we have counts for
9 papers
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.…
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
Andrea Zanette
Several practical applications of reinforcement learning involve an agent learning from past data without the possibility of further exploration. Often these applications require u…
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer +1
There has been growing progress on theoretical analyses for provably efficient learning in MDPs with linear function approximation, but much of the existing work has made strong as…
Learning Near Optimal Policies with Low Inherent Bellman Error
Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer +1
We study the exploration problem with approximate linear action-value functions in episodic reinforcement learning under the notion of low inherent Bellman error, a condition norma…