Education press releases — Press Release Database

Education

#Exploration: A study of count-based exploration for deep reinforcement learning

OpenAI 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

Nov 15 · 08:00OpenAI

AI · Energy

A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models

Abstract 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

Nov 11 · 08:00OpenAI

Education

RL²: Fast reinforcement learning via slow reinforcement learning

Abstract 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

Nov 9 · 08:00OpenAI

Education

Variational lossy autoencoder

Abstract 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

Nov 8 · 08:00OpenAI

AI · Healthcare

Semi-supervised knowledge transfer for deep learning from private training data

Abstract 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

Oct 18 · 07:00OpenAI

AI · Education

Report from the self-organizing conference

Last week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.

Oct 13 · 07:00OpenAI

Education

Transfer from simulation to real world through learning deep inverse dynamics model

Abstract 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

Oct 11 · 07:00OpenAI

Education

Infrastructure for deep learning

Deep 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.

Aug 29 · 07:00OpenAI

AI · Education

Machine Learning Unconference

The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.

Aug 18 · 07:00OpenAI

AI · Education

Concrete AI safety problems

We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.

Jun 21 · 07:00OpenAI

AI · Education

Generative models

This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this post will tell you a bit more about generative models: what they are, why they are important, and where they might be going.

Jun 16 · 07:00OpenAI

Education

Adversarial training methods for semi-supervised text classification

OpenAI Abstract Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations. We extend

May 25 · 07:00OpenAI