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No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech, Evrard Garcelon, Michal Valko +2
Many popular reinforcement learning problems (e.g., navigation in a maze, some Atari games, mountain car) are instances of the episodic setting under its stochastic shortest path (…
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Gaussian processes (GP) are a well studied Bayesian approach for the optimization of black-box functions. Despite their effectiveness in simple problems, GP-based algorithms hardly…
Exploiting Structure of Uncertainty for Efficient Matroid Semi-Bandits
Pierre Perrault, Vianney Perchet, Michal Valko
We improve the efficiency of algorithms for stochastic \emph{combinatorial semi-bandits}. In most interesting problems, state-of-the-art algorithms take advantage of structural pro…
Optimistic optimization of a Brownian
Jean-Bastien Grill, Michal Valko, Rémi Munos
We address the problem of optimizing a Brownian motion. We consider a (random) realization of a Brownian motion with input space in . Given , our goal is to return an…