In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …

Constructing agents with planning capabilities has long been one of the main challenges in
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of
indispensable factors to achieve human-level or super-human AI systems. On the other …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Unsupervised learning of probabilistic models is a central yet challenging problem in
machine learning. Specifically, designing models with tractable learning, sampling …

In recent years there have been many successes of using deep representations in
reinforcement learning. Still, many of these applications use conventional architectures …