B. Scherrer

发表

Approximate Dynamic Programming Finally Performs Well in the Game of Tetris

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

Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems

B. Scherrer, C. Thiery, 2010, ICML.

Improvements on Learning Tetris with Cross Entropy

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

Building Controllers for Tetris

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

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

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

Performance bound for Approximate Optimistic Policy Iteration

B. Scherrer, C. Thiery, 2010 .

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

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

Softened Approximate Policy Iteration for Markov Games

Matthieu Geist, Bruno Scherrer, Olivier Pietquin, 2016, ICML.

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.

Planning Cooperative Homogeneous Multiagent Systems Using Markov Decision Processes

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

Convergence and rate of convergence of a foraging ant model

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

Partially Observable Markov Decision Processes

B. Scherrer, Alain Dutech, A. Dutech, 2013 .

Batch Reinforcement Learning in Markov Games: a Bellman Residual Approach

B. Scherrer, Bilal Piot, J. Pérolat, 2015 .

Recursive Least-Squares Off-policy Learning with Eligibility Traces

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

Abstraction Pathologies In Markov Decision Processes

B. Scherrer, O. Buffet, J. Hoffmann, 2013 .

Least-Squares λ Policy Iteration : optimisme et compromis biais-variance pour le contrôle optimal

B. Scherrer, C. Thiery, 2010 .

l1-penalized projected Bellman residual

B. Scherrer, M. Geist, 2018 .

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

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

A Theory of Regularized Markov Decision Processes pdf

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

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

P. Bartlett, B. Scherrer, Lin Chen, 2021, ArXiv.

Anderson Acceleration for Reinforcement Learning pdf

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

Leverage the Average: an Analysis of Regularization in RL pdf

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

Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning

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

Approximate Policy Iteration Schemes: A Comparison

Bruno Scherrer, B. Scherrer, 2014, ICML.

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

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

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

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

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.

A Dantzig Selector Approach to Temporal Difference Learning

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

Author manuscript, published in "European Wrokshop on Reinforcement Learning (EWRL 11) (2011)" ℓ1-penalized projected Bellman residual

B. Scherrer, M. Geist, 2011 .

Improved and Generalized Upper Bounds on the Complexity of Policy Iteration

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

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

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

Momentum in Reinforcement Learning

B. Scherrer, O. Pietquin, M. Geist, 2019, AISTATS.

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

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

Beyond the One Step Greedy Approach in Reinforcement Learning

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

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

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

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

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

How to Combine Tree-Search Methods in Reinforcement Learning

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

Non-Stationary Approximate Modified Policy Iteration

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

Approximate Modified Policy Iteration pdf

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

Performance Bounds for Lambda Policy Iteration pdf

Bruno Scherrer, B. Scherrer, 2007, ArXiv.

Improvements on Learning Tetris with Cross Entropy

B. Scherrer, C. Thiery, 2009, J. Int. Comput. Games Assoc..

Optimal control subsumes harmonic control

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

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

Bruno Scherrer, B. Scherrer, 2007 .

Convergence and rate of convergence of a simple ant model

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