76 citations · 471 across the 29 of their papers we have counts for
4 papers · 1 filter
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas, Pierre Fournier, Olivier Sigaud +2
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a l…
Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI
Pablo Ortega, Cedric Colas, Aldo Faisal
For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment. Natural tasks often re…
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep re…
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
In continuous action domains, standard deep reinforcement learning algorithms like DDPG suffer from inefficient exploration when facing sparse or deceptive reward problems. Convers…