TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains

Todd Hester

ISBN 10: 3319011677 / ISBN 13: 9783319011677

Published by Springer International Publishing, 2013

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research on Temporal Difference Reinforcement Learning for Robots Focuses on applying Reinforcement Learning to real-world problems, particularly learning on robots Presents the model-based Reinforcement Learning algorithm developed .

Seller Inventory # 4496071

Bibliographic Details

• Title: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
• Publisher: Springer International Publishing
• Publication Date: 2013
• Language: English
• ISBN 10: 3319011677
• ISBN 13: 9783319011677
• Binding: Gebunden
• Condition: New
• Seller catalogs:
  • Mathematik/Naturwissenschaften/Technik/Medizin

About this title

Synopsis

This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time.

Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent’s lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.