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We’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers self-interested yet collaborative...
We’re releasing two new OpenAI Baselines implementations: ACKTR and A2C. A2C is a synchronous, deterministic variant of Asynchronous Advanta...
Our Dota 2 result shows that self-play can catapult the performance of machine learning systems from far below human level to superhuman, gi...
We’ve created a bot which beats the world’s top professionals at 1v1 matches of Dota 2 under standard tournament rules. The bot learned the ...
RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward func...
We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploratio...
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better tha...
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim ...
We’re open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of roboti...
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...
Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful p...
We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with publis...
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...
We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym....