93 citations · 204 across the 8 of their papers we have counts for
8 papers
Toward Optimal Stratification for Stratified Monte-Carlo Integration
Alexandra Carpentier, Remi Munos
We consider the problem of adaptive stratified sampling for Monte Carlo integration of a noisy function, given a finite budget n of noisy evaluations to the function. We tackle in…
Selecting the State-Representation in Reinforcement Learning
Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko
The problem of selecting the right state-representation in a reinforcement learning problem is considered. Several models (functions mapping past observations to a finite set) of t…
Risk-Aversion in Multi-armed Bandits
Amir Sani, Alessandro Lazaric, Rémi Munos
Stochastic multi-armed bandits solve the Exploration-Exploitation dilemma and ultimately maximize the expected reward. Nonetheless, in many practical problems, maximizing the expec…
Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions
Alexandra Carpentier, Rémi Munos
We consider the problem of adaptive stratified sampling for Monte Carlo integration of a differentiable function given a finite number of evaluations to the function. We construct…
Regret Bounds for Restless Markov Bandits
Ronald Ortner, Daniil Ryabko, Peter Auer +1
We consider the restless Markov bandit problem, in which the state of each arm evolves according to a Markov process independently of the learner's actions. We suggest an algorithm…
On the Sample Complexity of Reinforcement Learning with a Generative Model
Mohammad Gheshlaghi Azar, Remi Munos, Bert Kappen
We consider the problem of learning the optimal action-value function in the discounted-reward Markov decision processes (MDPs). We prove a new PAC bound on the sample-complexity o…