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Motivated Reinforcement Learning

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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.

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