Diederik Roijers — accepted AI-conference papers

2019

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

Axel Abels, Diederik Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher

ICML 2019oral

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 relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforc…

Cited by 231 Source ↗PDF ↗code ↗Scholar ↗

2018

Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems

Eugenio Bargiacchi, Timothy Verstraeten, Diederik Roijers, Ann Nowé, Hado Hasselt

ICML 2018oral

Learning to coordinate between multiple agents is an important problem in many reinforcement learning problems. Key to learning to coordinate is exploiting loose couplings, i.e., conditional independences between agents. In this paper we study learning in repeated fully cooperative games, multi-agen…

Cited by 54 Source ↗PDF ↗Scholar ↗