Browse press releases
The newest releases come first, each with its full text here and a link to its original. Try the filters to narrow them down.
- OpenAIAdversarial attacks on neural network policiesCompanies: OpenAIOpenAI Abstract Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification. Such adversarial examples have been extensively studied in the context of computer vision applications. In this work, we show adversarial attacks are also effective when targeting neural network policies in reinforcement learning. Specifically, we show existing adversari
- OpenAITeam updateCompanies: OpenAIThe OpenAI team is now 45 people. Together, we’re pushing the frontier of AI capabilities—whether by validating novel ideas, creating new software systems, or deploying machine learning on robots.
- OpenAIFaulty reward functions in the wildCompanies: OpenAIReinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function.
- OpenAI#Exploration: A study of count-based exploration for deep reinforcement learningCompanies: OpenAIOpenAI Abstract Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state spaces, since most states will only occur once. Recent deep RL exploration strategies are able to deal wi
- OpenAIA connection between generative adversarial networks, inverse reinforcement learning, and energy-based modelsCompanies: OpenAIAbstract Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator. While the idea of learning cost functions is relatively new to the field of generative modeling, learning costs has long been studied in control and reinforcement learning (RL) domains, typically for imit
- OpenAIRL²: Fast reinforcement learning via slow reinforcement learningCompanies: OpenAIAbstract Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Rather than designing a "fast" reinforcement learning algorithm, we propose to repr
- OpenAIVariational lossy autoencoderCompanies: OpenAIAbstract Representation learning seeks to expose certain aspects of observed data in a learned representation that’s amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combini
- OpenAISemi-supervised knowledge transfer for deep learning from private training dataCompanies: OpenAIAbstract Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore reveal sensitive information. To address this problem, we demonstrate a generally applicable approach to providing strong privacy guarantees for training
- OpenAIReport from the self-organizing conferenceCompanies: OpenAILast week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.
- OpenAITransfer from simulation to real world through learning deep inverse dynamics modelCompanies: OpenAIAbstract Developing control policies in simulation is often more practical and safer than directly running experiments in the real world. This applies to policies obtained from planning and optimization, and even more so to policies obtained from reinforcement learning, which is often very data demanding. However, a policy that succeeds in simulation often doesn’t work when deployed on a real robot. Nevertheless, oft
- OpenAIInfrastructure for deep learningCompanies: OpenAIDeep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.
- OpenAIMachine Learning UnconferenceCompanies: OpenAIThe latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.