V
Victor Bapst
Researcher at Google
Publications - 50
Citations - 4754
Victor Bapst is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Artificial neural network. The author has an hindex of 23, co-authored 47 publications receiving 3481 citations. Previous affiliations of Victor Bapst include École Normale Supérieure & Goethe University Frankfurt.
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Posted Content
Sample Efficient Actor-Critic with Experience Replay
Ziyu Wang,Victor Bapst,Nicolas Heess,Volodymyr Mnih,Rémi Munos,Koray Kavukcuoglu,Nando de Freitas +6 more
TL;DR: This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.
Journal ArticleDOI
Unveiling the predictive power of static structure in glassy systems
Victor Bapst,Thomas Keck,Agnieszka Grabska-Barwinska,Craig Donner,Ekin D. Cubuk,Samuel S. Schoenholz,Annette Obika,Alexander Nelson,Trevor Back,Demis Hassabis,Pushmeet Kohli +10 more
TL;DR: This work determines the long-time evolution of a glassy system solely from the initial particle positions and without any handcrafted features, using graph neural networks as a powerful model, and shows that this method outperforms current state-of-the-art methods, generalizing over a wide range of temperatures, pressures and densities.
Posted Content
Relational Deep Reinforcement Learning.
Vinicius Zambaldi,David Raposo,Adam Santoro,Victor Bapst,Yujia Li,Igor Babuschkin,Karl Tuyls,David P. Reichert,Timothy P. Lillicrap,Edward Lockhart,Murray Shanahan,Victoria Langston,Razvan Pascanu,Matthew Botvinick,Oriol Vinyals,Peter W. Battaglia +15 more
TL;DR: This work introduces an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning.
Proceedings Article
Distral: robust multitask reinforcement learning
Yee Whye Teh,Victor Bapst,Wojciech Marian Czarnecki,John Quan,James Kirkpatrick,Raia Hadsell,Nicolas Heess,Razvan Pascanu +7 more
TL;DR: Distral as mentioned in this paper proposes to share a distilled policy that captures common behavior across tasks, and trains the shared policy by distillation to be the centroid of all task policies by optimizing a joint objective function.