40 citations · 58 across the 9 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
cs.LG2021★ 7 cited
Exploratory State Representation Learning
Astrid Merckling, Nicolas Perrin-Gilbert, Alex Coninx +1
Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to p…
cs.AI2021
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms
Alexandre Chenu, Nicolas Perrin-Gilbert, Stéphane Doncieux +1
Reinforcement learning agents need a reward signal to learn successful policies. When this signal is sparse or the corresponding gradient is deceptive, such agents need a dedicated…