158 citations · 680 across the 25 of their papers we have counts for
10 papers · 1 filter
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…
First-order and second-order variants of the gradient descent in a unified framework
Thomas Pierrot, Nicolas Perrin, Olivier Sigaud
In this paper, we provide an overview of first-order and second-order variants of the gradient descent method that are commonly used in machine learning. We propose a general frame…
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…
CEM-RL: Combining evolutionary and gradient-based methods for policy search
Aloïs Pourchot, Olivier Sigaud
Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search. The former is widely applicable and rather stable, but suffers…
Importance mixing: Improving sample reuse in evolutionary policy search methods
Aloïs Pourchot, Nicolas Perrin, Olivier Sigaud
Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due t…
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…
Accuracy-based Curriculum Learning in Deep Reinforcement Learning
Pierre Fournier, Olivier Sigaud, Mohamed Chetouani +1
In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a r…
Policy Search in Continuous Action Domains: an Overview
Olivier Sigaud, Freek Stulp
Continuous action policy search is currently the focus of intensive research, driven both by the recent success of deep reinforcement learning algorithms and the emergence of compe…
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
Alexandre Péré, Sébastien Forestier, Olivier Sigaud +1
Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These explorati…
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…