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How the Mind Comes into BeingIntroducing Cognitive Science from a Functional and Computational Perspective$
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Martin V. Butz and Esther F. Kutter

Print publication date: 2017

Print ISBN-13: 9780198739692

Published to Oxford Scholarship Online: July 2017

DOI: 10.1093/acprof:oso/9780198739692.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 28 July 2021

Behavior is Reward-oriented

Behavior is Reward-oriented

Chapter:
(p.109) Chapter 5 Behavior is Reward-oriented
Source:
How the Mind Comes into Being
Author(s):

Martin V. Butz

Esther F. Kutter

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198739692.003.0005

Delving further into development, adaptation, and learning, this chapter considers the potential of reward-oriented optimization of behavior. Reinforcement learning (RL) is motivated from the Rescorla–Wagner model in psychology and behaviorism. Next, a detailed introduction to RL in artificial systems is provided. It is shown when and how RL works, but also current shortcomings and challenges are discussed. In conclusion, the chapter emphasizes that behavioral optimization and reward-based behavioral adaptations can be well-accomplished with RL. However, to be able to solve more challenging planning problems and to enable flexible, goal-oriented behavior, hierarchically and modularly structured models about the environment are necessary. Such models then also enable the pursuance of abstract reasoning and of thoughts that are fully detached from the current environmental state. The challenge remains how such models may actually be learned and structured.

Keywords:   reward, behaviorism, Rescorla–Wagner model, reinforcement learning, Markov decision processes, behavioral policies, temporal difference learning, eligibility traces, model-based reinforcement learning, hierarchies, state factorizations, policy gradients

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