Coevolutive planning in markov decision processes. - Bruno Scherrer; François Charpillet (AAMAS, 2002)

Cooperative Co-Learning: A Model-Based Approach for Solving Multi Agent Reinforcement Problems. - Bruno Scherrer; François Charpillet (ICTAI, 2002)

A heuristic approach for solving decentralized-POMDP: assessment on the pursuit problem. - Iadine Chades; Bruno Scherrer; François Charpillet (SAC, 2002)

Parallel asynchronous distributed computations of optimal control in large state space Markov Decision processes. - Bruno Scherrer (ESANN, 2003)

Planning Cooperative Homogeneous Multiagent Systems Using Markov Decision Processes. - Iadine Chades; Bruno Scherrer; François Charpillet (ICEIS (2), 2003)

Modular self-organization for a long-living autonomous agent. - Bruno Scherrer (IJCAI, 2003)

Apprentissage de représentation et auto-organisation modulaire pour un agent autonome. - Bruno Scherrer (2003)

Asynchronous neurocomputing for optimal control and reinforcement learning with large state spaces. - Bruno Scherrer (Neurocomputing, 2005)

Modular self-organization - Bruno Scherrer (CoRR, 2006)

Convergence and rate of convergence of a simple ant model. - Amine M. Boumaza; Bruno Scherrer (AAMAS, 2007)

Convergence and rate of convergence of a foraging ant model. - Amine M. Boumaza; Bruno Scherrer (IEEE Congress on Evolutionary Computation, 2007)

Optimal control subsumes harmonic control. - Amine M. Boumaza; Bruno Scherrer (ICRA, 2007)

Performance Bounds for Lambda Policy Iteration - Bruno Scherrer (CoRR, 2007)

Biasing Approximate Dynamic Programming with a Lower Discount Factor. - Marek Petrik; Bruno Scherrer (NIPS, 2008)

Embedded Harmonic Control for Trajectory Planning in Large Environments. - César Torres-Huitzil; Bernard Girau; Amine M. Boumaza; Bruno Scherrer (ReConFig, 2008)

Analyse d'un algorithme d'intelligence en essaim pour le fourragement. - Amine M. Boumaza; Bruno Scherrer (Rev. d'Intelligence Artif., 2008)

Building Controllers for Tetris. - Christophe Thiery; Bruno Scherrer (J. Int. Comput. Games Assoc., 2009)

Improvements on Learning Tetris with Cross Entropy. - Christophe Thiery; Bruno Scherrer (J. Int. Comput. Games Assoc., 2009)

Construction d'un joueur artificiel pour Tetris. - Christophe Thiery; Bruno Scherrer (Rev. d'Intelligence Artif., 2009)

Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view. - Bruno Scherrer (ICML, 2010)

Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems. - Christophe Thiery; Bruno Scherrer (ICML, 2010)

ℓ1-Penalized Projected Bellman Residual. - Matthieu Geist; Bruno Scherrer (EWRL, 2011)

Recursive Least-Squares Learning with Eligibility Traces. - Bruno Scherrer; Matthieu Geist (EWRL, 2011)

Classification-based Policy Iteration with a Critic. - Victor Gabillon; Alessandro Lazaric; Mohammad Ghavamzadeh; Bruno Scherrer (ICML, 2011)

A Dantzig Selector Approach to Temporal Difference Learning. - Matthieu Geist; Bruno Scherrer; Alessandro Lazaric; Mohammad Ghavamzadeh (ICML, 2012)

Approximate Modified Policy Iteration. - Bruno Scherrer; Victor Gabillon; Mohammad Ghavamzadeh; Matthieu Geist (ICML, 2012)

On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes. - Bruno Scherrer; Boris Lesner (NIPS, 2012)

On the Use of Non-Stationary Policies for Infinite-Horizon Discounted Markov Decision Processes - Bruno Scherrer (CoRR, 2012)

Approximate Dynamic Programming Finally Performs Well in the Game of Tetris. - Victor Gabillon; Mohammad Ghavamzadeh; Bruno Scherrer (NIPS, 2013)

On the Performance Bounds of some Policy Search Dynamic Programming Algorithms. - Bruno Scherrer (CoRR, 2013)

Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee. - Bruno Scherrer; Matthieu Geist (CoRR, 2013)

Tight Performance Bounds for Approximate Modified Policy Iteration with Non-Stationary Policies - Boris Lesner; Bruno Scherrer (CoRR, 2013)

Performance bounds for λ policy iteration and application to the game of Tetris. - Bruno Scherrer (J. Mach. Learn. Res., 2013)

Approximate Policy Iteration Schemes: A Comparison. - Bruno Scherrer (ICML, 2014)

Local Policy Search in a Convex Space and Conservative Policy Iteration as Boosted Policy Search. - Bruno Scherrer; Matthieu Geist (ECML/PKDD (3), 2014)

Rate of Convergence and Error Bounds for LSTD(λ). - Manel Tagorti; Bruno Scherrer (CoRR, 2014)

Off-policy learning with eligibility traces: a survey. - Matthieu Geist; Bruno Scherrer (J. Mach. Learn. Res., 2014)

Modified policy iteration algorithms are not strongly polynomial for discounted dynamic programming. - Eugene A. Feinberg; Jefferson Huang; Bruno Scherrer (Oper. Res. Lett., 2014)

Non-Stationary Approximate Modified Policy Iteration. - Boris Lesner; Bruno Scherrer (ICML, 2015)

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games. - Julien Pérolat; Bruno Scherrer; Bilal Piot; Olivier Pietquin (ICML, 2015)

On the Rate of Convergence and Error Bounds for LSTD( ( lambda )). - Manel Tagorti; Bruno Scherrer (ICML, 2015)

Approximate modified policy iteration and its application to the game of Tetris. - Bruno Scherrer; Mohammad Ghavamzadeh; Victor Gabillon; Boris Lesner; Matthieu Geist (J. Mach. Learn. Res., 2015)

Recherche locale de politique dans un espace convexe. - Bruno Scherrer; Matthieu Geist (Rev. d'Intelligence Artif., 2015)

Contributions algorithmiques au contrôle optimal stochastique à temps discret et horizon infini. - Bruno Scherrer (2016)

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games. - Julien Pérolat; Bilal Piot; Bruno Scherrer; Olivier Pietquin (AISTATS, 2016)

Softened Approximate Policy Iteration for Markov Games. - Julien Pérolat; Bilal Piot; Matthieu Geist; Bruno Scherrer; Olivier Pietquin (ICML, 2016)

Improved and Generalized Upper Bounds on the Complexity of Policy Iteration. - Bruno Scherrer (Math. Oper. Res., 2016)

Beyond the One-Step Greedy Approach in Reinforcement Learning. - Yonathan Efroni; Gal Dalal; Bruno Scherrer; Shie Mannor (ICML, 2018)

Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning. - Yonathan Efroni; Gal Dalal; Bruno Scherrer; Shie Mannor (NeurIPS, 2018)

Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning. - Yonathan Efroni; Gal Dalal; Bruno Scherrer; Shie Mannor (CoRR, 2018)

Anderson Acceleration for Reinforcement Learning. - Matthieu Geist; Bruno Scherrer (CoRR, 2018)

How to Combine Tree-Search Methods in Reinforcement Learning. - Yonathan Efroni; Gal Dalal; Bruno Scherrer; Shie Mannor (AAAI, 2019)

Stability guarantees for nonlinear discrete-time systems controlled by approximate value iteration. - Romain Postoyan; Mathieu Granzotto; Lucian Busoniu; Bruno Scherrer; Dragan Nesic; Jamal Daafouz (CDC, 2019)

A Theory of Regularized Markov Decision Processes. - Matthieu Geist; Bruno Scherrer; Olivier Pietquin (ICML, 2019)

Momentum in Reinforcement Learning. - Nino Vieillard; Bruno Scherrer; Olivier Pietquin; Matthieu Geist (AISTATS, 2020)

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (NeurIPS, 2020)

Leverage the Average: an Analysis of Regularization in RL. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (CoRR, 2020)

Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm. - Lin Chen; Bruno Scherrer; Peter L. Bartlett (CoRR, 2021)

AlphaBeta is not as good as you think: a new probabilistic model to better analyze deterministic game-solving algorithms. - Raphaël Boige; Amine M. Boumaza; Bruno Scherrer (CoRR, 2025)