19 citations · 66 across the 20 of their papers we have counts for
4 papers · 2 filters
Optimistic PAC Reinforcement Learning: the Instance-Dependent View
Andrea Tirinzoni, Aymen Al-Marjani, Emilie Kaufmann
Optimistic algorithms have been extensively studied for regret minimization in episodic tabular MDPs, both from a minimax and an instance-dependent view. However, for the PAC RL pr…
Near-Optimal Collaborative Learning in Bandits
Clémence Réda, Sattar Vakili, Emilie Kaufmann
This paper introduces a general multi-agent bandit model in which each agent is facing a finite set of arms and may communicate with other agents through a central controller in or…
Efficient Algorithms for Extreme Bandits
Dorian Baudry, Yoan Russac, Emilie Kaufmann
In this paper, we contribute to the Extreme Bandit problem, a variant of Multi-Armed Bandits in which the learner seeks to collect the largest possible reward. We first study the c…
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs
Andrea Tirinzoni, Aymen Al-Marjani, Emilie Kaufmann
In probably approximately correct (PAC) reinforcement learning (RL), an agent is required to identify an -optimal policy with probability . While minimax optimal algorithms…