Bruno Scherrer

François Charpillet, Bruno Scherrer, 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games

Bruno Scherrer, Olivier Pietquin, Bilal Piot, 2016, AISTATS.

Performance Bounds for Lambda Policy Iteration pdf

Bruno Scherrer, B. Scherrer, 2007, ArXiv.

Error Reducing Sampling in Reinforcement Learning

Shie Mannor, Bruno Scherrer, 2008, NIPS 2008.

Convergence of Online and Approximate Multiple-Step Lookahead Policy Iteration

Shie Mannor, Bruno Scherrer, Yonathan Efroni, 2018, EWRL 2018.

Asynchronous neurocomputing for optimal control and reinforcement learning with large state spaces

Bruno Scherrer, 2005, Neurocomputing.

Une étude comparative de quelques schémas d'approximation de type iterations sur les politiques

Bruno Scherrer, 2014 .

A heuristic approach for solving decentralized-POMDP: assessment on the pursuit problem

François Charpillet, Bruno Scherrer, Iadine Chades, 2002, SAC '02.

Convergence and rate of convergence of a simple ant model

Bruno Scherrer, Amine M. Boumaza, B. Scherrer, 2007, AAMAS '07.

Building Controllers for Tetris

Bruno Scherrer, Christophe Thiery, B. Scherrer, 2009, J. Int. Comput. Games Assoc..

Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning pdf

Shie Mannor, Bruno Scherrer, Yonathan Efroni, 2018, NIPS 2018.

On the Performance Bounds of some Policy Search Dynamic Programming Algorithms pdf

Bruno Scherrer, 2013, ArXiv.

Beyond the One Step Greedy Approach in Reinforcement Learning

Shie Mannor, Bruno Scherrer, Yonathan Efroni, 2018, ICML.

Approximate Modified Policy Iteration pdf

Matthieu Geist, Bruno Scherrer, Mohammad Ghavamzadeh, 2012, ICML.

Coevolutive planning in markov decision processes

François Charpillet, Bruno Scherrer, 2002, AAMAS '02.

Tight Performance Bounds for Approximate Modified Policy Iteration with Non-Stationary Policies pdf

Bruno Scherrer, Boris Lesner, 2013, ArXiv.

Analyse d'un algorithme d'intelligence en essaim pour le fourragement

Bruno Scherrer, Amine M. Boumaza, B. Scherrer, 2008, Rev. d'Intelligence Artif..

Momentum in Reinforcement Learning

Bruno Scherrer, Olivier Pietquin, Matthieu Geist, 2020, AISTATS.

Parallel asynchronous distributed computations of optimal control in large state space Markov Decision processes

Bruno Scherrer, 2003, ESANN.

Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning

Shie Mannor, Bruno Scherrer, Yonathan Efroni, 2018, NeurIPS.

Modified policy iteration algorithms are not strongly polynomial for discounted dynamic programming

Bruno Scherrer, Eugene A. Feinberg, Jefferson Huang, 2014, Oper. Res. Lett..

Apprentissage de représentation et auto-organisation modulaire pour un agent autonome

Bruno Scherrer, 2003 .

A Theory of Regularized Markov Decision Processes pdf

Matthieu Geist, Bruno Scherrer, Olivier Pietquin, 2019, ICML.

Optimal control subsumes harmonic control

Bruno Scherrer, Amine M. Boumaza, B. Scherrer, 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

Embedded Harmonic Control for Trajectory Planning in Large Environments

Bernard Girau, César Torres-Huitzil, Bruno Scherrer, 2008, 2008 International Conference on Reconfigurable Computing and FPGAs.

Performance bounds for λ policy iteration and application to the game of Tetris

Bruno Scherrer, B. Scherrer, 2013, J. Mach. Learn. Res..

Approximate Dynamic Programming Finally Performs Well in the Game of Tetris

Bruno Scherrer, Mohammad Ghavamzadeh, Victor Gabillon, 2013, NIPS.

Performance Bounds for Lambda Policy Iteration and Application to the Game of Tetris

Bruno Scherrer, B. Scherrer, 2007 .

Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee pdf

Matthieu Geist, Bruno Scherrer, 2013, ArXiv.

Approximate Policy Iteration Schemes: A Comparison

Bruno Scherrer, B. Scherrer, 2014, ICML.

New Results - Stochastic models

Olivier Buffet, Maan El Badaoui El Najjar, François Charpillet, 2008 .

Planning Cooperative Homogeneous Multiagent Systems Using Markov Decision Processes

François Charpillet, Bruno Scherrer, Iadine Chades, 2003, ICEIS.

Approximate Modied Policy Iteration

Matthieu Geist, Bruno Scherrer, Mohammad Ghavamzadeh, 2012 .

Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view pdf

Bruno Scherrer, B. Scherrer, 2010, ICML.

On the Rate of Convergence and Error Bounds for LSTD(\(\lambda\))

Bruno Scherrer, Manel Tagorti, 2015, ICML.

Convergence and rate of convergence of a foraging ant model

Bruno Scherrer, Amine M. Boumaza, B. Scherrer, 2007, 2007 IEEE Congress on Evolutionary Computation.

Off-policy learning with eligibility traces: a survey pdf

Matthieu Geist, Bruno Scherrer, B. Scherrer, 2013, J. Mach. Learn. Res..

Biasing Approximate Dynamic Programming with a Lower Discount Factor

Marek Petrik, Bruno Scherrer, B. Scherrer, 2008, NIPS.

Recursive Least-Squares Learning with Eligibility Traces

Matthieu Geist, Bruno Scherrer, 2011, EWRL.

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning

Bruno Scherrer, Matthieu Geist, Olivier Pietquin, 2020, NeurIPS.

Recherche locale de politique dans un espace convexe

Matthieu Geist, Bruno Scherrer, Matthieu Geist, 2015, Rev. d'Intelligence Artif..

On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes

Bruno Scherrer, Boris Lesner, B. Scherrer, 2012, NIPS.

On the Use of Non-Stationary Policies for Infinite-Horizon Discounted Markov Decision Processes pdf

Bruno Scherrer, 2012, ArXiv.

A Dantzig Selector Approach to Temporal Difference Learning

Matthieu Geist, Bruno Scherrer, Alessandro Lazaric, 2012, ICML.

Improvements on Learning Tetris with Cross Entropy

Bruno Scherrer, Christophe Thiery, B. Scherrer, 2009, J. Int. Comput. Games Assoc..

Non-Stationary Approximate Modified Policy Iteration

Bruno Scherrer, Boris Lesner, B. Scherrer, 2015, ICML.

Learning to use contextual information for solving POMDP

Bruno Scherrer, Alain Dutech, 2001 .

Rate of Convergence and Error Bounds for LSTD(λ) pdf

Bruno Scherrer, Manel Tagorti, 2014, ICML 2015.

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games

Bruno Scherrer, Olivier Pietquin, Bilal Piot, 2015, ICML.

Improved and Generalized Upper Bounds on the Complexity of Policy Iteration

Bruno Scherrer, B. Scherrer, 2013, Math. Oper. Res..

Construction d'un joueur artificiel pour Tetris

Bruno Scherrer, Christophe Thiery, 2009, Rev. d'Intelligence Artif..

Leverage the Average: an Analysis of Regularization in RL pdf

Bruno Scherrer, Matthieu Geist, Olivier Pietquin, 2020, ArXiv.

Approximate modified policy iteration and its application to the game of Tetris

Matthieu Geist, Bruno Scherrer, Mohammad Ghavamzadeh, 2015, J. Mach. Learn. Res..

Anderson Acceleration for Reinforcement Learning pdf

Matthieu Geist, Bruno Scherrer, B. Scherrer, 2018, EWRL 2018.

Stability guarantees for nonlinear discrete-time systems controlled by approximate value iteration

Dragan Nesic, Romain Postoyan, Jamal Daafouz, 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm pdf

Peter L. Bartlett, Bruno Scherrer, Lin Chen, 2021, ArXiv.

Block-synchronous Harmonic Control for Scalable Trajectory Planning

Bernard Girau, Bruno Scherrer, Amine Boumaza, 2008 .

Recursive Least-Squares Off-policy Learning with Eligibility Traces

Matthieu Geist, Bruno Scherrer, B. Scherrer, 2011 .