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Quantifying generalization in reinforcement learni...
Spinning Up in Deep RL
Learning concepts with energy functions
Plan online, learn offline: Efficient learning and...
Reinforcement learning with prediction-based rewar...
Learning complex goals with iterated amplification...
OpenAI Scholars 2019: Applications open
OpenAI Fellows Winter 2019 & Interns Summer 2019
FFJORD: Free-form continuous dynamics for scalable...
OpenAI Scholars 2018: Final projects
The International 2018: Results
Large-scale study of curiosity-driven learning
OpenAI Five Benchmark: Results
Learning dexterity
Variational option discovery algorithms
OpenAI Scholars 2018: Meet our Scholars
OpenAI Five Benchmark
Glow: Better reversible generative models
Learning Montezuma’s Revenge from a single demonst...
OpenAI Five
Retro Contest: Results
Learning policy representations in multiagent syst...
Improving language understanding with unsupervised...
GamePad: A learning environment for theorem provin...
OpenAI Fellows Fall 2018
Gym Retro
AI and compute
AI safety via debate
Evolved Policy Gradients
Gotta Learn Fast: A new benchmark for generalizati...
Retro Contest
Variance reduction for policy gradient with action...
Improving GANs using optimal transport
Report from the OpenAI hackathon
On first-order meta-learning algorithms
Reptile: A scalable meta-learning algorithm
OpenAI Scholars
Some considerations on learning to explore via met...
Multi-Goal Reinforcement Learning: Challenging rob...
Ingredients for robotics research
OpenAI hackathon
OpenAI supporters
Preparing for malicious uses of AI
Interpretable machine learning through teaching
Discovering types for entity disambiguation
Requests for Research 2.0
Scaling Kubernetes to 2,500 nodes
Block-sparse GPU kernels
Learning sparse neural networks through L₀ regular...
Interpretable and pedagogical examples
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