activity
20062019
most citedDynamic Weights in Multi-Objective Deep Reinforcement Learning

59 citations · 124 across the 4 of their papers we have counts for

collaborators

5 papers

q-bio.PE2019

Towards a phylogenetic measure to quantify HIV incidence

Pieter Libin, Nassim Versbraegen, Ana B. Abecasis +3

One of the cornerstones in combating the HIV pandemic is being able to assess the current state and evolution of local HIV epidemics. This remains a complex problem, as many HIV in…

cs.LG2018★ 59 cited

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

Axel Abels, Diederik M. Roijers, Tom Lenaerts +2

Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the…

q-bio.PE2016★ 39 cited

Modeling contact networks of patients and MRSA spread in Swedish hospitals

Luis E C Rocha, Vikramjit Singh, Markus Esch +4

Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection that only in the European Union affects about 150,000 patients and causes extra costs of 380 mi…

q-bio.PE2007★ 25 cited

Evolution of Complexity

Carlos Gershenson, Tom Lenaerts

The evolution of complexity has been a central theme for Biology [2] and Artificial Life research [1]. It is generally agreed that complexity has increased in our universe, giving…

nlin.AO2006★ 1 cited

Evolution of Complexity: Introduction to the Workshop

Carlos Gershenson, Tom Lenaerts

The evolution of complexity has been a central theme for Biology and Artificial Life (Bonner, 1988; Bedau et al., 2000). Complexification has been interpreted in different ways: as…