In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

In recent years, a specific machine learning method called deep learning has gained huge
attraction, as it has obtained astonishing results in broad applications such as pattern …

The theory of reinforcement learning provides a normative account, deeply rooted in
psychological and neuroscientific perspectives on animal behaviour, of how agents may …

We present the first deep learning model to successfully learn control policies directly from
high-dimensional sensory input using reinforcement learning. The model is a convolutional …

Recent work has shown that offline reinforcement learning (RL) can be formulated as a
sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via …

We study how representation learning can accelerate reinforcement learning from rich
observations, such as images, without relying either on domain knowledge or pixel …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn
directly from image observations. However, these high-dimensional observation spaces …

The recent advances in deep neural networks have led to effective vision-based
reinforcement learning methods that have been employed to obtain human-level controllers …

Abstract We introduce Embed to Control (E2C), a method for model learning and control of
non-linear dynamical systems from raw pixel images. E2C consists of a deep generative …