Follow
Olivier Sigaud
Olivier Sigaud
Professor in Computer Science, Sorbonne Université
Verified email at upmc.fr - Homepage
Title
Cited by
Cited by
Year
Markov decision processes in artificial intelligence
John Wiley & Sons, 2010
393*2010
Many regression algorithms, one unified model: A review
F Stulp, O Sigaud
Neural Networks 69, 60-79, 2015
2792015
Path integral policy improvement with covariance matrix adaptation
F Stulp, O Sigaud
arXiv preprint arXiv:1206.4621, 2012
2652012
CURIOUS: Intrinsically motivated multi-task multi-goal reinforcement learning
C Colas, P Fournier, O Sigaud, PY Oudeyer
239*2018
Anticipatory behavior in adaptive learning systems: From brains to individual and social behavior
MV Butz, O Sigaud, G Pezzulo, G Baldassarre
Lecture Notes In Artificial Intelligence, Springer, 2007
203*2007
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
C Colas, O Sigaud, PY Oudeyer
International Conference in Machine Learning (ICML), 2018
1912018
CEM-RL: Combining evolutionary and gradient-based methods for policy search
A Pourchot, O Sigaud
International Conference on Learning Representations (ICLR), 2018
1892018
Robot skill learning: From reinforcement learning to evolution strategies
F Stulp, O Sigaud
Paladyn, Journal of Behavioral Robotics 4 (1), 49-61, 2013
1802013
On-line regression algorithms for learning mechanical models of robots: a survey
O Sigaud, C Salaün, V Padois
Robotics and Autonomous Systems 59 (12), 1115-1129, 2011
1752011
Grounding large language models in interactive environments with online reinforcement learning
T Carta, C Romac, T Wolf, S Lamprier, O Sigaud, PY Oudeyer
arXiv preprint arXiv:2302.02662 (ICML 2023), 2023
1662023
Learning the structure of factored markov decision processes in reinforcement learning problems
T Degris, O Sigaud, PH Wuillemin
Proceedings of the 23rd international conference on Machine learning, 257-264, 2006
1622006
Learning classifier systems: a survey
O Sigaud, SW Wilson
Soft Computing 11, 1065-1078, 2007
1512007
Autotelic agents with intrinsically motivated goal-conditioned reinforcement learning: a short survey
C Colas, T Karch, O Sigaud, PY Oudeyer
Journal of Artificial Intelligence Research 74, 1159-1199, 2022
1382022
The problem with DDPG: understanding failures in deterministic environments with sparse rewards
G Matheron, N Perrin, O Sigaud
arXiv preprint arXiv:1911.11679, 2019
136*2019
Anticipatory behavior in adaptive learning systems: Foundations, Theories and Systems
MV Butz, O Sigaud, P Gérard
Lecture Notes in Artificial Intelligence,, 2003
1282003
Anticipatory behavior: Exploiting knowledge about the future to improve current behavior
MV Butz, O Sigaud, P Gérard
Anticipatory behavior in adaptive learning systems: Foundations, theories …, 2003
1272003
How many random seeds? statistical power analysis in deep reinforcement learning experiments
C Colas, O Sigaud, PY Oudeyer
arXiv preprint arXiv:1806.08295, 2018
1242018
Internal models and anticipations in adaptive learning systems
MV Butz, O Sigaud, P Gerard
Anticipatory behavior in adaptive learning systems: Foundations, theories …, 2003
1232003
Policy Search in Continuous Action Domains: an Overview
O Sigaud, F Stulp
Neural Networks, https://doi.org/10.1016/j.neunet.2019.01, 2018
1092018
Unsupervised learning of goal spaces for intrinsically motivated goal exploration
A Péré, S Forestier, O Sigaud, PY Oudeyer
arXiv preprint arXiv:1803.00781, 2018
1092018
The system can't perform the operation now. Try again later.
Articles 1–20