Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …

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

A generative recurrent neural network is quickly trained in an unsupervised manner to
model popular reinforcement learning environments through compressed spatio-temporal …

The success of deep learning depends on finding an architecture to fit the task. As deep
learning has scaled up to more challenging tasks, the architectures have become difficult to …

Humans rarely tackle every problem from scratch. Given this observation, the motivation for
this paper is to improve optimization performance through adaptive knowledge transfer …

The simple, but general formal theory of fun and intrinsic motivation and creativity (1990-
2010) is based on the concept of maximizing intrinsic reward for the active creation or …

We explore building generative neural network models of popular reinforcement learning
environments. Our world model can be trained quickly in an unsupervised manner to learn a …

Abstract This paper presents Natural Evolution Strategies (NES), a recent family of black-box
optimization algorithms that use the natural gradient to update a parameterized search …

We transform reinforcement learning (RL) into a form of supervised learning (SL) by turning
traditional RL on its head, calling this Upside Down RL (UDRL). Standard RL predicts …

This paper describes a variational free-energy formulation of (partially observable) Markov
decision problems in decision making under uncertainty. We show that optimal control can …