Arthur Guez
E455377
Arthur Guez is a machine learning researcher known for his contributions to deep reinforcement learning, including co-developing the Double DQN algorithm.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Arthur Guez canonical | 2 |
Statements (32)
| Predicate | Object |
|---|---|
| instanceOf |
machine learning researcher
ⓘ
person ⓘ |
| affiliation | DeepMind reinforcement learning team NERFINISHED ⓘ |
| coAuthorOf |
Bayes-Adaptive Monte-Carlo Planning and Learning in POMDPs
NERFINISHED
ⓘ
Bayes-Adaptive Planning in Markov Decision Processes NERFINISHED ⓘ Deep Reinforcement Learning with Double Q-learning NERFINISHED ⓘ Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search NERFINISHED ⓘ Information-Theoretic Regret Bounds for Online Nonparametric Regression NERFINISHED ⓘ Investigating Model-Free Planning in Human Choice Prediction NERFINISHED ⓘ Sample-based Search for Optimal Planning in Markov Decision Processes NERFINISHED ⓘ |
| coAuthorWith |
David Silver
NERFINISHED
ⓘ
Hado van Hasselt NERFINISHED ⓘ |
| coDeveloperOf | Double DQN algorithm NERFINISHED ⓘ |
| doctoralThesisTopic | Bayes-adaptive planning and learning in Markov decision processes NERFINISHED ⓘ |
| educatedAt | McGill University NERFINISHED ⓘ |
| employer | Google DeepMind NERFINISHED ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
deep reinforcement learning ⓘ machine learning ⓘ reinforcement learning ⓘ |
| gender | male ⓘ |
| hasAcademicAdvisor | Doina Precup NERFINISHED ⓘ |
| hasCitation | Deep Reinforcement Learning with Double Q-learning (2015) NERFINISHED ⓘ |
| knownFor | Double DQN algorithm NERFINISHED ⓘ |
| memberOf | DeepMind NERFINISHED ⓘ |
| nationality | French ⓘ |
| notableWork | Deep Reinforcement Learning with Double Q-learning NERFINISHED ⓘ |
| researchInterest |
Monte Carlo tree search
NERFINISHED
ⓘ
model-based reinforcement learning ⓘ planning in reinforcement learning ⓘ |
| worksOn |
applications of deep RL to games
ⓘ
sample-efficient reinforcement learning ⓘ |
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.