34 citations · 38 across the 3 of their papers we have counts for
13 papers
Offline Reinforcement Learning Hands-On
Louis Monier, Jakub Kmec, Alexandre Laterre +4
Offline Reinforcement Learning (RL) aims to turn large datasets into powerful decision-making engines without any online interactions with the environment. This great promise has m…
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…
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…
Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects
Nicolas Le Hir, Olivier Sigaud, Alban Laflaquière
Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object…