Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …

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 …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Within the context of multiple mobile, and networked robot systems, this chapter explores the
current state of the art. After a brief introduction, we first examine architectures for multirobot …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …

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 …

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 …

Intelligent human agents exist in a coop-erative social environment that facilitates learning.
They learn not only by trialand-error, but also through cooperation by sharing instantaneous …

Intelligent agents are employed as the central characters in this new introductory text.
Beginning with elementary reactive agents, Nilsson gradually increases their cognitive …