| Markov decision processes in artificial intelligence John Wiley & Sons, 2010 | 442* | 2010 |
| Many regression algorithms, one unified model: A review F Stulp, O Sigaud Neural Networks 69, 60-79, 2015 | 322 | 2015 |
| 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 | 309 | 2023 |
| Path integral policy improvement with covariance matrix adaptation F Stulp, O Sigaud arXiv preprint arXiv:1206.4621 (ICML 2012), 2012 | 283 | 2012 |
| CURIOUS: Intrinsically motivated multi-task multi-goal reinforcement learning C Colas, P Fournier, O Sigaud, PY Oudeyer In International conference on machine learning (pp. 1331-1340)., 2018 | 274* | 2018 |
| CEM-RL: Combining evolutionary and gradient-based methods for policy search A Pourchot, O Sigaud International Conference on Learning Representations (ICLR), 2018 | 232 | 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 | 210* | 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 | 200 | 2018 |
| Robot skill learning: From reinforcement learning to evolution strategies F Stulp, O Sigaud Paladyn, Journal of Behavioral Robotics 4 (1), 49-61, 2013 | 195 | 2013 |
| 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 | 192 | 2022 |
| 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 | 180 | 2011 |
| 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 | 176 | 2006 |
| The problem with DDPG: understanding failures in deterministic environments with sparse rewards G Matheron, N Perrin, O Sigaud arXiv preprint arXiv:1911.11679, 2019 | 167* | 2019 |
| Learning classifier systems: a survey O Sigaud, SW Wilson Soft Computing 11 (11), 1065-1078, 2007 | 154 | 2007 |
| How many random seeds? statistical power analysis in deep reinforcement learning experiments C Colas, O Sigaud, PY Oudeyer arXiv preprint arXiv:1806.08295, 2018 | 149 | 2018 |
| Anticipatory behavior in adaptive learning systems: Foundations, Theories and Systems MV Butz, O Sigaud, P Gérard Lecture Notes in Artificial Intelligence,, 2003 | 132 | 2003 |
| 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 | 128 | 2003 |
| Internal models and anticipations in adaptive learning systems MV Butz, O Sigaud, P Gerard Anticipatory behavior in adaptive learning systems: Foundations, theories …, 2003 | 124 | 2003 |
| A hitchhiker's guide to statistical comparisons of reinforcement learning algorithms C Colas, O Sigaud, PY Oudeyer arXiv preprint arXiv:1904.06979, 2019 | 119 | 2019 |
| Policy Search in Continuous Action Domains: an Overview O Sigaud, F Stulp Neural Networks, https://doi.org/10.1016/j.neunet.2019.01, 2018 | 118 | 2018 |
Pierre-Yves OudeyerResearch director, InriaVerified email at inria.fr
