Matteo Papini | DeepAI

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∙ 05/22/2023

Offline Primal-Dual Reinforcement Learning for Linear MDPs

Offline Reinforcement Learning (RL) aims to learn a near-optimal policy ...

0 Germano Gabbianelli, et al. ∙

∙ 10/24/2022

Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees

We study the problem of representation learning in stochastic contextual...

0 Andrea Tirinzoni, et al. ∙

∙ 07/18/2022

Online Learning with Off-Policy Feedback

We study the problem of online learning in adversarial bandit problems u...

3 Germano Gabbianelli, et al. ∙

∙ 05/27/2022

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits

We study the Bayesian regret of the renowned Thompson Sampling algorithm...

12 Gergely Neu, et al. ∙

∙ 10/27/2021

Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection

We study the role of the representation of state-action value functions ...

12 Matteo Papini, et al. ∙

∙ 04/08/2021

Leveraging Good Representations in Linear Contextual Bandits

The linear contextual bandit literature is mostly focused on the design ...

5 Matteo Papini, et al. ∙

∙ 12/15/2020

Policy Optimization as Online Learning with Mediator Feedback

Policy Optimization (PO) is a widely used approach to address continuous...

3 Alberto Maria Metelli, et al. ∙

∙ 12/06/2019

Risk-Averse Trust Region Optimization for Reward-Volatility Reduction

In real-world decision-making problems, for instance in the fields of fi...

0 Lorenzo Bisi, et al. ∙

∙ 09/09/2019

Gradient-Aware Model-based Policy Search

Traditional model-based reinforcement learning approaches learn a model ...

0 Pierluca D'Oro, et al. ∙

∙ 07/17/2019

Feature Selection via Mutual Information: New Theoretical Insights

Mutual information has been successfully adopted in filter feature-selec...

0 Mario Beraha, et al. ∙

∙ 05/08/2019

Smoothing Policies and Safe Policy Gradients

Policy gradient algorithms are among the best candidates for the much an...

0 Matteo Papini, et al. ∙

∙ 09/17/2018

Policy Optimization via Importance Sampling

Policy optimization is an effective reinforcement learning approach to s...

0 Alberto Maria Metelli, et al. ∙

∙ 06/14/2018

Stochastic Variance-Reduced Policy Gradient

In this paper, we propose a novel reinforcement- learning algorithm cons...

0 Matteo Papini, et al. ∙

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