Pietquin, Olivier

Deep Q-learning From Demonstrations

Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 889

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 478

Audiolm: A language modeling approach to audio generation

Borsos, Z., Marinier, R., Vincent, D., Kharitonov, E., Pietquin, O., Sharifi, M., Roblek, D., Teboul, O., Grangier, D., Tagliasacchi, M., et al. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing

Speech Recognition and SynthesisMusic and Audio Processing

Cited by 432

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

Andrychowicz, M., Raichuk, A., Stańczyk, P., Orsini, M., Girgin, S., Marinier, R., Hussenot, L., Geist, M., Pietquin, O., Michalski, M., et al. (2020) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 115

Machine learning for spoken dialogue systems

Lemon, O., & Pietquin, O. (2007) · Interspeech 2007

Speech and dialogue systemsSpeech Recognition and Synthesis

Cited by 104

Observational Learning by Reinforcement Learning

Borsa, D., Piot, B., Munos, R., & Pietquin, O. (2017) · arXiv

Reinforcement Learning in RoboticsChild and Animal Learning Development

Cited by 47

Observational Learning by Reinforcement Learning

Borsa, D., Heess, N., Piot, B., Liu, S., Hasenclever, L., Munos, R., & Pietquin, O. (2019) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsChild and Animal Learning Development

Cited by 47

What Matters for Adversarial Imitation Learning?

Orsini, M., Raichuk, A., Hussenot, L., Vincent, D., Dadashi, R., Girgin, S., Geist, M., Bachem, O., Pietquin, O., & Andrychowicz, M. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 32