A growing body of evidence suggests that neural networks employed in deep reinforcement
learning (RL) gradually lose their plasticity, the ability to learn from new data; however, the …

Deep reinforcement learning (deep RL) combines deep learning and reinforcement
learning, in which artificial agents learn to solve sequential decision-making problems. In the …

This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with
the foundations of the theory and building it up, this is essential reading for any scientists …

Realistic simulators are critical for training and verifying robotics systems. While most of the
contemporary simulators are hand-crafted, a scaleable way to build simulators is to use …

Predicting future video frames is extremely challenging, as there are many factors of
variation that make up the dynamics of how frames change through time. Previously …

We use reinforcement learning in simulation to obtain a driving system controlling a full-size
real-world vehicle. The driving policy takes RGB images from a single camera and their …

We propose a novel policy update that combines regularized policy optimization with model
learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the …

Model-based reinforcement learning (MBRL) with model-predictive control or online
planning has shown great potential for locomotion control tasks in terms of both sample …

Inattentional blindness is the psychological phenomenon that causes one to miss things in
plain sight. It is a consequence of the selective attention in perception that lets us remain …

It has been established that diverse behaviors spanning the controllable subspace of an
Markov decision process can be trained by rewarding a policy for being distinguishable from …