Search Results for author: Urtzi Ayesta

Found 3 papers, 0 papers with code

Tabular and Deep Learning for the Whittle Index

no code implementations4 Jun 2024 Francisco Robledo Relaño, Vivek Borkar, Urtzi Ayesta, Konstantin Avrachenkov

The Whittle index policy is a heuristic that has shown remarkably good performance (with guaranteed asymptotic optimality) when applied to the class of problems known as Restless Multi-Armed Bandit Problems (RMABPs).

Deep Learning Q-Learning

Deep reinforcement learning for weakly coupled MDP's with continuous actions

no code implementations3 Jun 2024 Francisco Robledo, Urtzi Ayesta, Konstantin Avrachenkov

This paper introduces the Lagrange Policy for Continuous Actions (LPCA), a reinforcement learning algorithm specifically designed for weakly coupled MDP problems with continuous action spaces.

Deep Reinforcement Learning reinforcement-learning

Improving the performance of heterogeneous data centers through redundancy

no code implementations3 Mar 2020 Elene Anton, Urtzi Ayesta, Matthieu Jonckheere, Ina Verloop

As such, our result is the first in showing that redundancy can improve the stability and hence performance of a system when copies are non-i. i. d..

Networking and Internet Architecture Probability

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