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One-shot imitation learning
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 systems against them can be difficult.
Adversarial attacks on neural network policies
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.
OpenAI and Microsoft
We’re working with Microsoft to start running most of our large-scale experiments on Azure.
#Exploration: A study of count-based exploration for deep reinforcement learning
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
Semi-supervised knowledge transfer for deep learning from private training data
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.
Transfer from simulation to real world through learning deep inverse dynamics model
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.