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α-Rank: Multi-Agent Evaluation by Evolution

Scientific reports, 2019-07, Vol.9 (1), p.9937-29, Article 9937

2019

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From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning

Foundations and trends in machine learning, 2014-01, Vol.7 (1), p.1-129

Munos, Rémi

2014

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A distributional code for value in dopamine-based reinforcement learning

Nature (London), 2020-01, Vol.577 (7792), p.671-675

2020

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Pure exploration in finitely-armed and continuous-armed bandits

Theoretical computer science, 2011-04, Vol.412 (19), p.1832-1852

2011

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Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling

Machine learning, 2021-03, Vol.110 (3), p.559-618

2021

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KULLBACK-LEIBLER UPPER CONFIDENCE BOUNDS FOR OPTIMAL SEQUENTIAL ALLOCATION

The Annals of statistics, 2013-06, Vol.41 (3), p.1516-1541

2013

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Navigating the landscape of multiplayer games

Nature communications, 2020-11, Vol.11 (1), p.5603-5603, Article 5603

2020

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Exploration–exploitation tradeoff using variance estimates in multi-armed bandits

Theoretical computer science, 2009-04, Vol.410 (19), p.1876-1902

2009

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Finite-Sample Analysis of Least-Squares Policy Iteration

Journal of machine learning research, 2012-10, Vol.13, p.3041-3074

2012

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X-Armed Bandits

Journal of machine learning research, 2011-05, Vol.12, p.1655-1695

2011

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Analysis of Classification-based Policy Iteration Algorithms

Journal of machine learning research, 2016-01, Vol.17, p.1-30

2016

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Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

Machine learning, 2013-06, Vol.91 (3), p.325-349

2013

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Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path

Machine learning, 2008-04, Vol.71 (1), p.89-129

2008

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Performance Bounds in $L_p$‐norm for Approximate Value Iteration

SIAM journal on control and optimization, 2007-01, Vol.46 (2), p.541-561

Munos, Rémi

2007

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Regret bounds for restless Markov bandits

Theoretical computer science, 2014-11, Vol.558, p.62-76

2014

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Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values

Automatica (Oxford), 2018-06, Vol.92, p.100-108

2018

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Spectral bandits

Journal of machine learning research, 2020-01

2020

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Policy Gradient in Continuous Time

Journal of machine learning research, 2006-05, Vol.7, p.771-791

Munos, Rémi

2006

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Variable Resolution Discretization in Optimal Control

Machine learning, 2002-11, Vol.49 (2-3), p.291-323

2002

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Q(\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}) with Off-Policy Corrections

Algorithmic Learning Theory, 2016, p.305-320

2016

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Linear Regression with Random Projections

Journal of machine learning research, 2012-09, Vol.13 (1), p.2735-2772

2012

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Mastering the game of Stratego with model-free multiagent reinforcement learning

Science (American Association for the Advancement of Science), 2022-12, Vol.378 (6623), p.990-996

2022

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Minimax number of strata for online stratified sampling: The case of noisy samples

Theoretical computer science, 2014-11, Vol.558, p.77-106

2014

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Distributional Reinforcement Learning With Quantile Regression

Proceedings of the ... AAAI Conference on Artificial Intelligence, 2018, Vol.32 (1)

2018

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Optimistic planning with an adaptive number of action switches for near-optimal nonlinear control

Engineering applications of artificial intelligence, 2018-01, Vol.67, p.355-367

2018

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α-Rank: Multi-Agent Evaluation by Evolution

From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning

A distributional code for value in dopamine-based reinforcement learning

Pure exploration in finitely-armed and continuous-armed bandits

Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling

KULLBACK-LEIBLER UPPER CONFIDENCE BOUNDS FOR OPTIMAL SEQUENTIAL ALLOCATION

Navigating the landscape of multiplayer games

Exploration–exploitation tradeoff using variance estimates in multi-armed bandits

Finite-Sample Analysis of Least-Squares Policy Iteration

X-Armed Bandits

Analysis of Classification-based Policy Iteration Algorithms

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path

Performance Bounds in $L_p$‐norm for Approximate Value Iteration

Regret bounds for restless Markov bandits

Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values

Spectral bandits

Policy Gradient in Continuous Time

Variable Resolution Discretization in Optimal Control

Q(\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}) with Off-Policy Corrections

Linear Regression with Random Projections

Mastering the game of Stratego with model-free multiagent reinforcement learning

Minimax number of strata for online stratified sampling: The case of noisy samples

Distributional Reinforcement Learning With Quantile Regression

Optimistic planning with an adaptive number of action switches for near-optimal nonlinear control

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