… The reinforcement learning paradigm is a popular way to … , as is common in other machine
learning contexts. While significant … focuses on transfer between reinforcement learning tasks. …

… domains, where we informally define a domain to be a setting for a group of semantically
similar tasks. Such cross-domain … options across continuous reinforcement learning domains. …

… a problem that may otherwise be too difficult to learn from scratch. In this article, we present
a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

… of reinforcement learning from a computer-science perspective. It is written to be accessible
to researchers familiar with machine learning… world as a problem domain ever since the field …

… Reinforcement Learning Background This monograph focuses on transfer learning in
reinforcement learning domains; some RL background is necessary. Our goal in this chapter is to …

We consider the problem of generalization in reinforcement learning where visual aspects of
the observations might differ, eg when there are different backgrounds or change in contrast…

… have proven effective at solving reinforcement learning (RL) … , a standard RL benchmark
domain based on robot soccer. In … Our results in this domain demonstrate that NEAT discovers …

… the past on simple domains like grid … reinforcement learning algorithms with on-line algorithms
based on their empirical performance in a complex, continuous, noisy, multiagent domain, …

… domain transfer. Also, while our work focused on discrete and deterministic environments,
we believe that transfer learning … difficulty of applying reinforcement learning methods directly …