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#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.

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

On the quantitative analysis of decoder-based generative models

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

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

RL²: Fast reinforcement learning via slow reinforcement learning

RL²: Fast reinforcement learning via slow reinforcement learning

Variational lossy autoencoder

Variational lossy autoencoder

Extensions and limitations of the neural GPU

Extensions and limitations of the neural GPU

Electric Cars, Solar & Clean Energy | Tesla United Kingdom - Tesla

Electric Cars, Solar & Clean Energy | Tesla United Kingdom  Tesla

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

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.

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

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