We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient...

We’re providing 6–10 stipends and mentorship to individuals from underrepresented groups to study deep learning full-time for 3 months an...

We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for ou...

Come to OpenAI’s office in San Francisco’s Mission District for talks and a hackathon on Saturday, March 3rd.

We’re excited to welcome new donors to OpenAI.

We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate ...

We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically ...

We’ve built a system for automatically figuring out which object is meant by a word by having a neural network decide if the word belongs...

We’re releasing a new batch of seven unsolved problems which have come up in the course of our research at OpenAI.

We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weigh...

We’ve developed a hierarchical reinforcement learning algorithm that learns high-level actions useful for solving a range of tasks, allow...

Our latest robotics techniques allow robot controllers, trained entirely in simulation and deployed on physical robots, to react to unpla...

We’ve found that self-play allows simulated AIs to discover physical skills like tackling, ducking, faking, kicking, catching, and diving...

We show that for the task of simulated robot wrestling, a meta-learning agent can learn to quickly defeat a stronger non-meta-learning ag...

We’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers self-interested yet collaborat...