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Matej Vecerík
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2010 – 2019
- 2018
- [c2]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:
Deep Q-learning From Demonstrations. AAAI 2018: 3223-3230 - [i5]Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerík, Todd Hester, Cosmin Paduraru, Yuval Tassa:
Safe Exploration in Continuous Action Spaces. CoRR abs/1801.08757 (2018) - 2017
- [c1]Andrei A. Rusu, Matej Vecerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell:
Sim-to-Real Robot Learning from Pixels with Progressive Nets. CoRL 2017: 262-270 - [i4]Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, Martin A. Riedmiller:
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation. CoRR abs/1704.03073 (2017) - [i3]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:
Learning from Demonstrations for Real World Reinforcement Learning. CoRR abs/1704.03732 (2017) - [i2]Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin A. Riedmiller:
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. CoRR abs/1707.08817 (2017) - 2016
- [i1]Andrei A. Rusu, Matej Vecerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell:
Sim-to-Real Robot Learning from Pixels with Progressive Nets. CoRR abs/1610.04286 (2016)
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