6.3k citations
- University of California, BerkeleyUS18 papers
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- Berkeley CollegeUS4 papers
- Uber AI (United States)US4 papers
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- Institut national de recherche en sciences et technologies du numériqueFR3 papers
- McGill UniversityCA3 papers
- Microsoft (United States)US3 papers
- Mila - Quebec Artificial Intelligence InstituteCA3 papers
- The University of Texas at AustinUS3 papers
5 papers · 1 filter
Quantifying Generalization in Reinforcement Learning
Karl Cobbe, Oleg Klimov, Chris Hesse +2
In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for bo…
A Spectral Regularizer for Unsupervised Disentanglement
Aditya Ramesh, Youngduck Choi, Yann LeCun
A generative model with a disentangled representation allows for independent control over different aspects of the output. Learning disentangled representations has been a recent t…
Exploration by Random Network Distillation
Yuri Burda, Harrison Edwards, Amos Storkey +1
We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error o…
Large-Scale Study of Curiosity-Driven Learning
Yuri Burda, Harri Edwards, Deepak Pathak +3
Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense…
DeepType: Multilingual Entity Linking by Neural Type System Evolution
Jonathan Raiman, Olivier Raiman
The wealth of structured (e.g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence. So far, in…