158 citations · 680 across the 25 of their papers we have counts for
5 papers · 1 filter
Learning Compositional Neural Programs with Recursive Tree Search and Planning
Thomas Pierrot, Guillaume Ligner, Scott Reed +6
We propose a novel reinforcement learning algorithm, AlphaNPI, that incorporates the strengths of Neural Programmer-Interpreters (NPI) and AlphaZero. NPI contributes structural bia…
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms
Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science. This paper presents a hitchhiker's guide to rig…
Investigating Generalisation in Continuous Deep Reinforcement Learning
Chenyang Zhao, Olivier Sigaud, Freek Stulp +1
Deep Reinforcement Learning has shown great success in a variety of control tasks. However, it is unclear how close we are to the vision of putting Deep RL into practice to solve r…
Interactively shaping robot behaviour with unlabeled human instructions
Anis Najar, Olivier Sigaud, Mohamed Chetouani
In this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions. We ground the meaning of in…
CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments
Pierre Fournier, Olivier Sigaud, Cédric Colas +1
In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where…