Supporting state-of-the-art AI research requires balancing rapid prototy...
We propose a novel policy update that combines regularized policy
optimi...
Credit assignment in reinforcement learning is the problem of measuring ...
Model-based planning is often thought to be necessary for deep, careful
...
Log-based predictive maintenance of computing centers is a main concern
...
Reliably transmitting messages despite information loss due to a noisy
c...
Value estimation is a critical component of the reinforcement learning (...
In reinforcement learning, we can learn a model of future observations a...
We describe TF-Replicator, a framework for distributed machine learning
...
In spite of remarkable progress in deep latent variable generative model...
Stochastic video prediction is usually framed as an extrapolation proble...
Natural language processing has made significant inroads into learning t...
We propose a formulation of visual localization that does not require
co...
A neural network (NN) is a parameterised function that can be tuned via
...
In model-based reinforcement learning, generative and temporal models of...
A key challenge in model-based reinforcement learning (RL) is to synthes...
We describe the DeepMind Kinetics human action video dataset. The datase...
Learning to navigate in complex environments with dynamic elements is an...