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Faster physics in Python
We’re open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of roboti...
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Learning from human preferences
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 complex...
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Learning to cooperate, compete, and communicate
Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful p...
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OpenAI Baselines: DQN
We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with publis...
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Robots that learn
We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it...
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Roboschool
We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym....
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Unsupervised sentiment neuron
We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next...
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Spam detection in the physical world
We’ve created the world’s first Spam-detecting AI trained entirely in simulation and deployed on a physical robot....
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Evolution strategies as a scalable alternative to reinforcement learning
We’ve discovered that evolution strategies (ES), an optimization technique that’s been known for decades, rivals the performance of standard...
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Distill
We’re excited to support today’s launch of Distill, a new kind of journal aimed at excellent communication of machine learning results (nove...
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Learning to communicate
In this post we’ll outline new OpenAI research in which agents develop their own language....
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Attacking machine learning with adversarial examples
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake;...