Matej Vecerík

According to our database, Matej Vecerík authored at least 6 papers between 2017 and 2018.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2018

Safe Exploration in Continuous Action Spaces.

Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerík, Todd Hester, Cosmin Paduraru, Yuval Tassa

CoRR, 2018

Deep Q-learning From Demonstrations.

Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

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

Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin A. Riedmiller

CoRR, 2017

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation.

Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, Martin A. Riedmiller

CoRR, 2017

Learning from Demonstrations for Real World Reinforcement Learning.

Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

CoRR, 2017

Sim-to-Real Robot Learning from Pixels with Progressive Nets.

Andrei A. Rusu, Matej Vecerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell

Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017