Motivated Reinforcement Learning

Curious Characters for Multiuser Games

By Kathryn E. Merrick , Mary Lou Maher

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This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended, virtual world.

Full Description

  • ISBN13: 978-3-5408-9186-4
  • 220 Pages
  • User Level: Science
  • Publication Date: June 12, 2009
  • Available eBook Formats: PDF
  • eBook Price: $99.00
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Full Description
Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments – the goal being to understand how machines can develop new skills and achieve goals that were not predefined by human engineers. In particular, this book describes how motivated reinforcement learning agents can be used in computer games for the design of non-player characters that can adapt their behaviour in response to unexpected changes in their environment. This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended virtual world. Researchers in artificial intelligence, machine learning and artificial life will benefit from this book, as will practitioners working on complex, dynamic systems – in particular multiuser, online games.
Table of Contents

Table of Contents

  1. An Overview of Motivation and Reinforcement Learning.
  2. Modelling Motivation for Reinforcement Learning.
  3. Performance Metrics for Motivated Reinforcement Learning.
  4. Applying Motivated Reinforcement Learning in a Simulated Game World.
  5. Scalability of Motivated Reinforcement Learning in Complex and Dynamic Environments.
  6. Motivated Reinforcement Learning in an Open
  7. Ended Virtual World.
  8. Conclusion.
  9. App. A, Details of the Experimental Method.
  10. App. B, Additional Experimental Results in Open
  11. Ended Virtual Worlds.
  12. Glossary.
  13. References.
  14. Index
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