8 citations · 9 across the 6 of their papers we have counts for
6 papers
Online Planning in POMDPs with State-Requests
Raphael Avalos, Eugenio Bargiacchi, Ann Nowé +2
In key real-world problems, full state information is sometimes available but only at a high cost, like activating precise yet energy-intensive sensors or consulting humans, thereb…
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…
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
Axel Abels, Elias Fernandez Domingos, Ann Nowé +1
Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influenc…
Composing Reinforcement Learning Policies, with Formal Guarantees
Florent Delgrange, Guy Avni, Anna Lukina +3
We propose a novel framework to controller design in environments with a two-level structure: a known high-level graph ("map") in which each vertex is populated by a Markov decisio…
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth
Qingshuang Sun, Denis Steckelmacher, Yuan Yao +2
Communication plays a vital role in multi-agent systems, fostering collaboration and coordination. However, in real-world scenarios where communication is bandwidth-limited, existi…
An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems
Glenn Ceusters, Muhammad Andy Putratama, Rüdiger Franke +2
Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-spec…