Andrychowicz, Marcin

Learning to learn by gradient descent by gradient descent

Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., & de Freitas, N. (2016) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 1,486

Hindsight experience replay

Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, P., & Zaremba, W. (2017) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 1,355

Overcoming Exploration in Reinforcement Learning with Demonstrations

Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA)

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 712

Solving Rubik's Cube with a Robot Hand

OpenAI, Akkaya, I., Andrychowicz, M., Chociej, M., Litwin, M., McGrew, B., Petron, A., Paino, A., Plappert, M., Powell, G., et al. (2019) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 602

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

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