Andrea Zanette | DeepAI

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∙ 07/10/2023

Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data

In some applications of reinforcement learning, a dataset of pre-collect...

0 Ruiqi Zhang, et al. ∙

∙ 11/10/2022

When is Realizability Sufficient for Off-Policy Reinforcement Learning?

Model-free algorithms for reinforcement learning typically require a con...

0 Andrea Zanette, et al. ∙

∙ 06/01/2022

Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning

The Q-learning algorithm is a simple and widely-used stochastic approxim...

0 Andrea Zanette, et al. ∙

∙ 03/24/2022

Bellman Residual Orthogonalization for Offline Reinforcement Learning

We introduce a new reinforcement learning principle that approximates th...

0 Andrea Zanette, et al. ∙

∙ 08/19/2021

Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning

Actor-critic methods are widely used in offline reinforcement learning p...

0 Andrea Zanette, et al. ∙

∙ 07/21/2021

Design of Experiments for Stochastic Contextual Linear Bandits

In the stochastic linear contextual bandit setting there exist several m...

0 Andrea Zanette, et al. ∙

∙ 03/24/2021

Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation

Policy optimization methods are popular reinforcement learning algorithm...

0 Andrea Zanette, et al. ∙

∙ 12/14/2020

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...

0 Andrea Zanette, et al. ∙

∙ 08/18/2020

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration

There has been growing progress on theoretical analyses for provably eff...

2 Andrea Zanette, et al. ∙

∙ 02/29/2020

Learning Near Optimal Policies with Low Inherent Bellman Error

We study the exploration problem with approximate linear action-value fu...

15 Andrea Zanette, et al. ∙

∙ 11/03/2019

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...

0 Andrea Zanette, et al. ∙

∙ 11/01/2019

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration

We consider the exploration-exploitation dilemma in finite-horizon reinf...

0 Andrea Zanette, et al. ∙

∙ 01/01/2019

Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

Strong worst-case performance bounds for episodic reinforcement learning...

0 Andrea Zanette, et al. ∙

∙ 11/25/2018

Robust Super-Level Set Estimation using Gaussian Processes

This paper focuses on the problem of determining as large a region as po...

0 Andrea Zanette, et al. ∙

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