On February 2, we held our first Spinning Up Workshop as part of our new education initiative at OpenAI.
We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.
Our second class of OpenAI Fellows has wrapped up, with each Fellow going from a machine learning beginner to core OpenAI contributor in the course of a 6-month apprenticeship. We are currently reviewing applications on a rolling basis for our next round of OpenAI Fellows Summer 2019.
Our first cohort of OpenAI Fellows has concluded, with each Fellow going from a machine learning beginner to core OpenAI contributor in the course of a 6-month apprenticeship.
We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms. It consists of a growing suite of environments (from simulated robots to Atari games), and a site for comparing and reproducing results.
We hosted the first OpenAI Robotics Symposium on April 27, 2019.
arXiv:2606. 27163v1 Announce Type: cross Abstract: I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding.
arXiv:2602. 13977v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots.
Reinforcement learning (RL) is an exciting concept as well as a remarkable success story worth sharing. However, RL builds on rather complex interactions between different objects that play out over s...
RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.
OpenAI Academy is expanding its offerings with new learning paths tailored for employees, developers, leaders, educators, and students. These paths aim to help participants build and demonstrate practical AI skills. The initiative broadens access to AI education across diverse audiences.
RLLBC-Lib is an educational code library designed to lower the entry barrier for students learning reinforcement learning (RL) in the context of learning-based control. It offers a comprehensive collection of tabular RL methods to reinforce theoretical foundations, followed by a deep RL library that mirrors the same design principles to highlight parallels between simple and state‑of‑the‑art approaches. The library also includes implementations that illustrate core RL principles, contrast RL with other learning‑based control methods, and serve as a foundation for programming assignments with automated grading.