RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward f...

We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This explora...

We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better ...

We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a cla...

We’re open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of rob...

One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a comp...

Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two usefu...

We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with pub...

We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing...

We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym.

We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the n...

We’ve created the world’s first Spam-detecting AI trained entirely in simulation and deployed on a physical robot.

We’ve discovered that evolution strategies (ES), an optimization technique that’s been known for decades, rivals the performance of stand...

We’re excited to support today’s launch of Distill, a new kind of journal aimed at excellent communication of machine learning results (n...

In this post we’ll outline new OpenAI research in which agents develop their own language.