Isabella's AI-Powered News Aggregator

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 goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve develop...

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 properties: first, there is a natural curriculum—the difficulty of the environment is determined by the skill of your competitors (and if you’re competing agains...

UCB exploration via Q-ensembles

OpenAI Baselines: DQN

We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its variants.

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 done once.

Roboschool

We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym.

Equivalence between policy gradients and soft Q-learning

Stochastic Neural Networks for hierarchical reinforcement learning

Unsupervised sentiment neuron

We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews.

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.

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 reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences.

One-shot imitation learning

Distill

We’re excited to support today’s launch of Distill, a new kind of journal aimed at excellent communication of machine learning results (novel or existing).

Learning to communicate

In this post we’ll outline new OpenAI research in which agents develop their own language.

Emergence of grounded compositional language in multi-agent populations

Prediction and control with temporal segment models

Third-person imitation learning

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; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing syst...

Adversarial attacks on neural network policies

Team update

The 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.

PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications

Faulty reward functions in the wild

Reinforcement 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.

Universe

We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.

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

OpenAI and Microsoft

We’re working with Microsoft to start running most of our large-scale experiments on Azure.

On the quantitative analysis of decoder-based generative models

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

RL²: Fast reinforcement learning via slow reinforcement learning

Variational lossy autoencoder

Extensions and limitations of the neural GPU