activity
20162022
most citedA unified view of entropy-regularized Markov decision processes

98 citations · 127 across the 10 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.LG2019

Induction of Subgoal Automata for Reinforcement Learning

Daniel Furelos-Blanco, Mark Law, Alessandra Russo +2

In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL). Our method relies on inducing an automaton whose transitions are…

cs.AI2019

Generalized Planning with Positive and Negative Examples

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson

Generalized planning aims at computing an algorithm-like structure (generalized plan) that solves a set of multiple planning instances. In this paper we define negative examples fo…

cs.AI201912 cited

Hierarchical Finite State Controllers for Generalized Planning

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson

Finite State Controllers (FSCs) are an effective way to represent sequential plans compactly. By imposing appropriate conditions on transitions, FSCs can also represent generalized…

cs.AI20196 cited

Generalized Planning With Procedural Domain Control Knowledge

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson

Generalized planning is the task of generating a single solution that is valid for a set of planning problems. In this paper we show how to represent and compute generalized plans…

cs.NI2019

A Flexible Machine Learning-Aware Architecture for Future WLANs

Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta +3

Lots of hopes have been placed on Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of large volumes of data, ML is expected to deal with the…

stat.AP2019

Decision Tree Learning for Uncertain Clinical Measurements

Cecília Nunes, Hélène Langet, Mathieu De Craene +3

Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known decision support tool, owing to their interpretability, fundamental in safety-critical…