Maximizing Learning Progress: An Internal Reward System for Development

  title={Maximizing Learning Progress: An Internal Reward System for Development},
  author={Fr{\'e}d{\'e}ric Kaplan and Pierre-Yves Oudeyer},
  booktitle={Embodied Artificial Intelligence},
This chapter presents a generic internal reward system that drives an agent to increase the complexity of its behavior. This reward system does not reinforce a predefined task. Its purpose is to drive the agent to progress in learning given its embodiment and the environment in which it is placed. The dynamics created by such a system are studied first in a simple environment and then in the context of active vision. 
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Robot bouncing : On the interaction between body and environmental dynamics

  • M. Lungarella, L. Berthouze
  • this volume
  • 2004
1 Excerpt

Proceedings of the 3 rd international workshop on Epigenetic Robotics : Modeling cognitive development in robotic systems

  • C. Prince, L. Berthouze, H. Kozima, D. Bullock, G. Stojanov, C. Balkenius
  • 2003

An evolutionary active-vision system

  • T. Kato, D. Floreano
  • Proceedings of the congress on evolutionary…
  • 2001
1 Excerpt

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