4 citations · 5 across the 10 of their papers we have counts for
4 papers · 1 filter
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution
Senne Deproost, Denis Steckelmacher, Ann Nowé
With Deep Reinforcement Learning (DRL) being increasingly considered for the control of real-world systems, the lack of transparency of the neural network at the core of RL becomes…
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…
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks
Yannick Molinghen, Raphaël Avalos, Mark Van Achter +2
We introduce the Laser Learning Environment (LLE), a collaborative multi-agent reinforcement learning environment in which coordination is central. In LLE, agents depend on each ot…
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
Florent Delgrange, Ann Nowé, Guillermo A. Pérez
Although deep reinforcement learning (DRL) has many success stories, the large-scale deployment of policies learned through these advanced techniques in safety-critical scenarios i…