8 citations · 13 across the 7 of their papers we have counts for
7 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…
When Do Off-Policy and On-Policy Policy Gradient Methods Align?
Davide Mambelli, Stephan Bongers, Onno Zoeter +2
Policy gradient methods are widely adopted reinforcement learning algorithms for tasks with continuous action spaces. These methods succeeded in many application domains, however,…
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
Zuzanna Osika, Jazmin Zatarain Salazar, Diederik M. Roijers +2
We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse…
Towards a Unifying Model of Rationality in Multiagent Systems
Robert Loftin, Mustafa Mert Çelikok, Frans A. Oliehoek
Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another. To design suc…
Safe Multi-agent Learning via Trapping Regions
Aleksander Czechowski, Frans A. Oliehoek
One of the main challenges of multi-agent learning lies in establishing convergence of the algorithms, as, in general, a collection of individual, self-serving agents is not guaran…
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning
Markus Peschl, Arkady Zgonnikov, Frans A. Oliehoek +1
Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions. However, st…