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- OpenAIGlow: Better reversible generative modelsCompanies: OpenAIWe introduce Glow, a reversible generative model which uses invertible 1x1 convolutions. It extends previous work on reversible generative models and simplifies the architecture. Our model can generate realistic high resolution images, supports efficient sampling, and discovers features that can be used to manipulate attributes of data. We’re releasing code for the model and an online visualization tool so people can explore and build on these results.
- OpenAILearning Montezuma’s Revenge from a single demonstrationCompanies: OpenAIWe’ve trained an agent to achieve a high score of 74,500 on Montezuma’s Revenge from a single human demonstration, better than any previously published result. Our algorithm is simple: the agent plays a sequence of games starting from carefully chosen states from the demonstration, and learns from them by optimizing the game score using PPO, the same reinforcement learning algorithm that underpins OpenAI Five.
- OpenAIOpenAI FiveCompanies: OpenAIOur team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2.
- OpenAIRetro Contest: ResultsCompanies: OpenAIThe first run of our Retro Contest—exploring the development of algorithms that can generalize from previous experience—is now complete.
- OpenAILearning policy representations in multiagent systemsCompanies: OpenAIOpenAI Abstract Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data. Our framework casts agent modeling
- OpenAIImproving language understanding with unsupervised learningCompanies: OpenAIWe’ve obtained state-of-the-art results on a suite of diverse language tasks with a scalable, task-agnostic system, which we’re also releasing. Our approach is a combination of two existing ideas: transformers and unsupervised pre-training. These results provide a convincing example that pairing supervised learning methods with unsupervised pre-training works very well; this is an idea that many have explored in the past, and we hope our result motivates further research into applying this idea on larger and more diverse datasets.
- OpenAIGamePad: A learning environment for theorem provingCompanies: OpenAIOpenAI Abstract In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize p
- OpenAIOpenAI Fellows Fall 2018Companies: OpenAIWe’re now accepting applications for the next cohort of OpenAI Fellows, a program which offers a compensated 6-month apprenticeship in AI research at OpenAI.
- OpenAIGym RetroCompanies: OpenAIWe’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games. This brings our publicly-released game count from around 70 Atari games and 30 Sega games to over 1,000 games across a variety of backing emulators. We’re also releasing the tool we use to add new games to the platform.
- OpenAIAI and computeCompanies: OpenAIWe’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period)[^footnote-correction]. Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities.
- OpenAIAI safety via debateCompanies: OpenAIWe’re proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins.
- OpenAIEvolved Policy GradientsCompanies: OpenAIWe’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room from where it was placed during training.