8 citations · 21 across the 9 of their papers we have counts for
13 papers
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
Andrea Tirinzoni, Matteo Papini, Ahmed Touati +2
We study the problem of representation learning in stochastic contextual linear bandits. While the primary concern in this domain is usually to find realizable representations (i.e…
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path
Liyu Chen, Andrea Tirinzoni, Matteo Pirotta +1
We study the sample complexity of learning an -optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner has access…
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification
Clémence Réda, Andrea Tirinzoni, Rémy Degenne
We study the problem of the identification of m arms with largest means under a fixed error rate (fixed-confidence Top-m identification), for misspecified linear bandit models.…
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano +3
We study the role of the representation of state-action value functions in regret minimization in finite-horizon Markov Decision Processes (MDPs) with linear structure. We first de…
A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs
Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric
We derive a novel asymptotic problem-dependent lower-bound for regret minimization in finite-horizon tabular Markov Decision Processes (MDPs). While, similar to prior work (e.g., f…
Meta-Reinforcement Learning by Tracking Task Non-stationarity
Riccardo Poiani, Andrea Tirinzoni, Marcello Restelli
Many real-world domains are subject to a structured non-stationarity which affects the agent's goals and the environmental dynamics. Meta-reinforcement learning (RL) has been shown…