132 citations · 183 across the 12 of their papers we have counts for
13 papers
Explainable AI Based Diagnosis of Poisoning Attacks in Evolutionary Swarms
Mehrdad Asadi, Roxana Rădulescu, Ann Nowé
Swarming systems, such as for example multi-drone networks, excel at cooperative tasks like monitoring, surveillance, or disaster assistance in critical environments, where autonom…
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
Florian Felten, Umut Ucak, Hicham Azmani +10
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting object…
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Peter Vamplew, Cameron Foale, Conor F. Hayes +9
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the u…
Bridging the Gap Between Single and Multi Objective Games
Willem Röpke, Carla Groenland, Roxana Rădulescu +2
A classic model to study strategic decision making in multi-agent systems is the normal-form game. This model can be generalised to allow for an infinite number of pure strategies…
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning
Mathieu Reymond, Conor F. Hayes, Lander Willem +8
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement l…
On Nash Equilibria in Normal-Form Games With Vectorial Payoffs
Willem Röpke, Diederik M. Roijers, Ann Nowé +1
We provide an in-depth study of Nash equilibria in multi-objective normal form games (MONFGs), i.e., normal form games with vectorial payoffs. Taking a utility-based approach, we a…