OpenAI Blog

Evolved Policy Gradients

We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss…

Gotta Learn Fast: A new benchmark for generalization in RL

Retro Contest

We’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize…

Variance reduction for policy gradient with action-dependent factorized baselines

Report from the OpenAI hackathon

On March 3rd, we hosted our first hackathon with 100 members of the artificial intelligence community.

Improving GANs using optimal transport

On first-order meta-learning algorithms

Reptile: A scalable meta-learning algorithm

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

OpenAI Scholars

We’re providing 6–10 stipends and mentorship to individuals from underrepresented groups to study deep learning…

Some considerations on learning to explore via meta-reinforcement learning

Ingredients for robotics research

We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay,…

Multi-Goal Reinforcement Learning: Challenging robotics environments and request for research

OpenAI hackathon

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

OpenAI supporters

We’re excited to welcome new donors to OpenAI.

Preparing for malicious uses of AI

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

Interpretable machine learning through teaching

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

Discovering types for entity disambiguation

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

Requests for Research 2.0

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

Scaling Kubernetes to 2,500 nodes

Block-sparse GPU kernels

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

Learning sparse neural networks through L₀ regularization

Interpretable and pedagogical examples

Learning a hierarchy

We’ve developed a hierarchical reinforcement learning algorithm that learns high-level actions useful for solving a…

Generalizing from simulation

Our latest robotics techniques allow robot controllers, trained entirely in simulation and deployed on physical robots,…

Asymmetric actor critic for image-based robot learning

Sim-to-real transfer of robotic control with dynamics randomization

Domain randomization and generative models for robotic grasping

Meta-learning for wrestling

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

Competitive self-play

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

Nonlinear computation in deep linear networks

Learning to model other minds

We’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers…

Learning with opponent-learning awareness

OpenAI Baselines: ACKTR & A2C

We’re releasing two new OpenAI Baselines implementations: ACKTR and A2C. A2C is a synchronous, deterministic variant of…

More on Dota 2

Our Dota 2 result shows that self-play can catapult the performance of machine learning systems from far below human…

Dota 2

We’ve created a bot which beats the world’s top professionals at 1v1 matches of Dota 2 under standard tournament rules.…

Gathering human feedback

RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than…