Modeling Behavior Cycles as a Value System for Developmental Robots

Author:

Merrick Kathryn E.1

Affiliation:

1. School of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy, Canberra, Australia, k.merrick@adfa.edu.au

Abstract

The behavior of natural systems is governed by rhythmic behavior cycles at the biological, cognitive, and social levels. These cycles permit natural organisms to adapt their behavior to their environment for survival, behavioral efficiency, or evolutionary advantage. This article proposes a model of behavior cycles as the basis for motivated reinforcement learning in developmental robots. Motivated reinforcement learning is a machine learning technique that incorporates a value system with a trial-and-error learning component. Motivated reinforcement learning is a promising model for developmental robotics because it provides a way for artificial agents to build and adapt their skill-sets autonomously over time. However, new models and metrics are needed to scale existing motivated reinforcement learning algorithms to the complex, real-world environments inhabited by robots. This article presents two such models and an experimental evaluation on four Lego Mindstorms NXT robots. Results show that the robots can evolve measurable, structured behavior cycles adapted to their individual physical forms.

Publisher

SAGE Publications

Subject

Behavioral Neuroscience,Experimental and Cognitive Psychology

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Value systems for developmental cognitive robotics: A survey;Cognitive Systems Research;2017-03

2. COULD CLOUD TECHNOLOGY BE USEFUL IN AUTONOMOUS MENTAL DEVELOPMENTAL ROBOTICS? A CASE STUDY;International Journal of Robotics and Automation;2016

3. Conclusion and Future;Computational Models of Motivation for Game-Playing Agents;2016

4. A brief overview of evolutionary developmental robotics;Industrial Robot: An International Journal;2014-10-20

5. A Temporal Difference GNG-Based Algorithm That Can Learn to Control in Reinforcement Learning Environments;2013 12th International Conference on Machine Learning and Applications;2013-12

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