6.3k citations
- University of California, BerkeleyUS18 papers
- Google (United States)US11 papers
- Stanford UniversityUS8 papers
- Carnegie Mellon UniversityUS6 papers
- Berkeley CollegeUS4 papers
- Uber AI (United States)US4 papers
- ETH ZurichCH3 papers
- 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
6 papers · 1 filter
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman +2
This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in orde…
Long-Term Planning and Situational Awareness in OpenAI Five
Jonathan Raiman, Susan Zhang, Filip Wolski
Understanding how knowledge about the world is represented within model-free deep reinforcement learning methods is a major challenge given the black box nature of its learning pro…
Neural Network Surgery with Sets
Jonathan Raiman, Susan Zhang, Christy Dennison
The cost to train machine learning models has been increasing exponentially, making exploration and research into the correct features and architecture a costly or intractable ende…
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton +1
We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learnin…
Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov +4
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocu…
The Role of Cooperation in Responsible AI Development
Amanda Askell, Miles Brundage, Gillian Hadfield
In this paper, we argue that competitive pressures could incentivize AI companies to underinvest in ensuring their systems are safe, secure, and have a positive social impact. Ensu…