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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
Learning a hierarchy
Generalizing from simulation
Asymmetric actor critic for image-based robot lear...
Sim-to-real transfer of robotic control with dynam...
Domain randomization and generative models for rob...
Competitive self-play
Meta-learning for wrestling
Nonlinear computation in deep linear networks
Learning to model other minds
Learning with opponent-learning awareness
OpenAI Baselines: ACKTR & A2C
More on Dota 2
Dota 2
Gathering human feedback
Better exploration with parameter noise
Proximal Policy Optimization
Robust adversarial inputs
Hindsight Experience Replay
Teacher–student curriculum learning
Faster physics in Python
Learning from human preferences
Learning to cooperate, compete, and communicate
UCB exploration via Q-ensembles
OpenAI Baselines: DQN
Robots that learn
Roboschool
Equivalence between policy gradients and soft Q-le...
Stochastic Neural Networks for hierarchical reinfo...
Unsupervised sentiment neuron
Spam detection in the physical world
Evolution strategies as a scalable alternative to ...
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