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Valentin Dalibard
Researcher at University of Cambridge
Publications - 18
Citations - 3409
Valentin Dalibard is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 7, co-authored 16 publications receiving 1775 citations. Previous affiliations of Valentin Dalibard include Google.
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Journal ArticleDOI
Grandmaster level in StarCraft II using multi-agent reinforcement learning.
Oriol Vinyals,Igor Babuschkin,Wojciech Marian Czarnecki,Michael Mathieu,Andrew Dudzik,Junyoung Chung,David H. Choi,Richard E. Powell,Timo Ewalds,Petko Georgiev,Junhyuk Oh,Dan Horgan,Manuel Kroiss,Ivo Danihelka,Aja Huang,Laurent Sifre,Trevor Cai,John P. Agapiou,Max Jaderberg,Alexander Vezhnevets,Rémi Leblond,Tobias Pohlen,Valentin Dalibard,David Budden,Yury Sulsky,James Molloy,Tom Le Paine,Caglar Gulcehre,Ziyu Wang,Tobias Pfaff,Yuhuai Wu,Roman Ring,Dani Yogatama,Dario Wünsch,Katrina McKinney,Oliver Smith,Tom Schaul,Timothy P. Lillicrap,Koray Kavukcuoglu,Demis Hassabis,Chris Apps,David Silver +41 more
TL;DR: The agent, AlphaStar, is evaluated, which uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.
Patent
Population based training of neural networks
TL;DR: Population Based Training is presented, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance.
Proceedings ArticleDOI
BOAT: Building Auto-Tuners with Structured Bayesian Optimization
TL;DR: BOAT is presented, a framework which allows developers to build efficient bespoke auto-tuners for their system, in situations where generic auto- Tuners fail, and is a novel extension of the Bayesian optimization algorithm.
Proceedings ArticleDOI
A Generalized Framework for Population Based Training
Ang Li,Ola Spyra,Sagi Perel,Valentin Dalibard,Max Jaderberg,Chenjie Gu,David Budden,Tim Harley,Pramod Gupta +8 more
TL;DR: This work proposes a general, black-box PBT framework that distributes many asynchronous "trials" (a small number of training steps with warm-starting) across a cluster, coordinated by the PBT controller, and shows that the system achieves better accuracy and faster convergence compared to existing methods, given the same computational resource.
Proceedings ArticleDOI
PrefEdge: SSD Prefetcher for Large-Scale Graph Traversal
TL;DR: PrefEdge is presented, a prefetcher for graph algorithms that parallelises requests to derive maximum throughput from SSDs that performs up to 80% faster without the program complexity and the programmer effort needed for multi-threaded graph algorithms.