Search Results for author: Jean-bastien Grill

Found 11 papers, 7 papers with code

Monte-Carlo Tree Search as Regularized Policy Optimization

3 code implementations ICML 2020 Jean-bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos

The combination of Monte-Carlo tree search (MCTS) with deep reinforcement learning has led to significant advances in artificial intelligence.

reinforcement-learning

Planning in entropy-regularized Markov decision processes and games

1 code implementation NeurIPS 2019 Jean-bastien Grill, Omar Darwiche Domingues, Pierre Menard, Remi Munos, Michal Valko

We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the SmoothCruiser.

World Discovery Models

1 code implementation20 Feb 2019 Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo Avila Pires, Jean-bastien Grill, Florent Altché, Rémi Munos

As humans we are driven by a strong desire for seeking novelty in our world.

Optimistic optimization of a Brownian

no code implementations NeurIPS 2018 Jean-bastien Grill, Michal Valko, Rémi Munos

Given $W$, our goal is to return an $\epsilon$-approximation of its maximum using the smallest possible number of function evaluations, the sample complexity of the algorithm.

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning

no code implementations NeurIPS 2016 Jean-bastien Grill, Michal Valko, Remi Munos

We study the sampling-based planning problem in Markov decision processes (MDPs) that we can access only through a generative model, usually referred to as Monte-Carlo planning.

Black-box optimization of noisy functions with unknown smoothness

no code implementations NeurIPS 2015 Jean-bastien Grill, Michal Valko, Remi Munos

We study the problem of black-box optimization of a function $f$ of any dimension, given function evaluations perturbed by noise.

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