26 citations · 79 across the 21 of their papers we have counts for
3 papers · 2 filters
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon with states, and actions. The pe…
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation
Alain Durmus, Eric Moulines, Alexey Naumov +1
This paper provides a finite-time analysis of linear stochastic approximation (LSA) algorithms with fixed step size, a core method in statistics and machine learning. LSA is used t…
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
Daniil Tiapkin, Denis Belomestny, Eric Moulines +5
We propose the Bayes-UCBVI algorithm for reinforcement learning in tabular, stage-dependent, episodic Markov decision process: a natural extension of the Bayes-UCB algorithm by Kau…