Machine Learning & AI

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At OpenAI, we're working on learning-based robotics. This means developing methods to allow robots to learn new tasks quickly, without being programmed explicitly for a specific task.

Hindsight Experience Replay (2017)

Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba

NIPS 2017, Long Beach

We show how an off-policy reinforcement learning algorithm can learn from its own failures in a multi-goal environment.

One-Shot Imitation Learning (2017)

Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba

NIPS 2017, Long Beach

We show how a robot can identify a task shown by a human in virtual reality, and replicate it in previously unseen conditions.

Gym (2016)

OpenAI Gym is the industry-standard toolkit for benchmarking the performance of reinforcement learning algorithms.


The next big AI breakthroughs will require both new learning algorithms, and new infrastructure to scale machine learning to thousands of computers.

Machine Learning Systems at Scale (2017)

Thinking about the entirety of the stack is crucial for achieving scalable performance in distributed deep learning.

MLconf 2017, San Francisco

Building the Infrastructure that Powers the Future of AI (2017)

How OpenAI uses Kubernetes and TensorFlow to run machine learning experiments across thousands of machines

Keynote, KubeCon 2017, Berlin

Data Retrieval

Analytic Performance Model of a Main-Memory Index Structure (2016)

Bachelor's Thesis, Karlsruhe Institute of Technology