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Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
Actor-critic methods are widely used in offline reinforcement learning p...
08/19/2021 ∙ by Andrea Zanette, et al. ∙
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Design of Experiments for Stochastic Contextual Linear Bandits
In the stochastic linear contextual bandit setting there exist several m...
07/21/2021 ∙ by Andrea Zanette, et al. ∙
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Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation
Policy optimization methods are popular reinforcement learning algorithm...
03/24/2021 ∙ by Andrea Zanette, et al. ∙
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Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
Several practical applications of reinforcement learning involve an agen...
12/14/2020 ∙ by Andrea Zanette, et al. ∙
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Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
There has been growing progress on theoretical analyses for provably eff...
08/18/2020 ∙ by Andrea Zanette, et al. ∙
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Learning Near Optimal Policies with Low Inherent Bellman Error
We study the exploration problem with approximate linear action-value fu...
02/29/2020 ∙ by Andrea Zanette, et al. ∙
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Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
In order to make good decision under uncertainty an agent must learn fro...
11/03/2019 ∙ by Andrea Zanette, et al. ∙
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Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
We consider the exploration-exploitation dilemma in finite-horizon reinf...
11/01/2019 ∙ by Andrea Zanette, et al. ∙
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Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Strong worst-case performance bounds for episodic reinforcement learning...
01/01/2019 ∙ by Andrea Zanette, et al. ∙
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Robust Super-Level Set Estimation using Gaussian Processes
This paper focuses on the problem of determining as large a region as po...
11/25/2018 ∙ by Andrea Zanette, et al. ∙
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