Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

This paper surveys the field of reinforcement learning from a computer-science perspective.
It is written to be accessible to researchers familiar with machine learning. Both the historical …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

We review a range of techniques related to navigation of unmanned vehicles through
unknown environments with obstacles, especially those that rigorously ensure collision …

This book is a reader-friendly description of a viewpoint on human behavior which sees all
behavior as aimed at attaining goals. A wide variety of topics are treated (the theory is …

Learning, planning, and representing knowledge at multiple levels of temporal abstraction
are key, longstanding challenges for AI. In this paper we consider how these challenges can …

Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this
dissertation is to extend the state of the art of reinforcement learning and enable its …

The new approaches that have been developed recently for artificial intelligence (AI) arose
from work with mobile robots. This chapter outlines the context within which this work arose …

Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level
reasoning was necessary for the coupling of perception and action. In this traditional model …

This paper discusses why traditional reinforcement learning methods, and algorithms
applied to those models, result in poor performance in situated domains characterized by …