75 citations · 820 across the 81 of their papers we have counts for
17 papers · 1 filter
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
Jean Tarbouriech, Matteo Pirotta, Michal Valko +1
We investigate the exploration of an unknown environment when no reward function is provided. Building on the incremental exploration setting introduced by Lim and Auer [1], we def…
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli +1
In the contextual linear bandit setting, algorithms built on the optimism principle fail to exploit the structure of the problem and have been shown to be asymptotically suboptimal…
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer +1
There has been growing progress on theoretical analyses for provably efficient learning in MDPs with linear function approximation, but much of the existing work has made strong as…
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Marc Abeille, Alessandro Lazaric
We study the exploration-exploitation dilemma in the linear quadratic regulator (LQR) setting. Inspired by the extended value iteration algorithm used in optimistic algorithms for…
Improved Analysis of UCRL2 with Empirical Bernstein Inequality
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
We consider the problem of exploration-exploitation in communicating Markov Decision Processes. We provide an analysis of UCRL2 with Empirical Bernstein inequalities (UCRL2B). For…
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
Jean Tarbouriech, Matteo Pirotta, Michal Valko +1
One of the challenges in online reinforcement learning (RL) is that the agent needs to trade off the exploration of the environment and the exploitation of the samples to optimize…