no code implementations • 23 Oct 2021 • Sergio Valcarcel Macua, Ian Davies, Aleksi Tukiainen, Enrique Munoz de Cote
We propose a fully distributed actor-critic architecture, named Diff-DAC, with application to multitask reinforcement learning (MRL).
no code implementations • 9 Oct 2019 • Marcin B. Tomczak, Sergio Valcarcel Macua, Enrique Munoz de Cote, Peter Vrancx
In this work we establish conditions under which the parametric approximation of the critic does not introduce bias to the updates of surrogate objective.
no code implementations • 16 May 2019 • James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote
We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events.
no code implementations • 28 Oct 2017 • Sergio Valcarcel Macua, Aleksi Tukiainen, Daniel García-Ocaña Hernández, David Baldazo, Enrique Munoz de Cote, Santiago Zazo
We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named \textit{Diff-DAC}, with application to single-task and to average multitask reinforcement learning (MRL).
no code implementations • 28 Jul 2017 • Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, Enrique Munoz de Cote
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves.