65 citations · 198 across the 14 of their papers we have counts for
3 papers · 1 filter
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…