Adaptivity at every layer: a modular approach for evolving societies of learning autonomous systems
Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
Proceedings of the 2011 workshop on Organic computing
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We present a framework that is able to handle system and environmental changes by learning autonomously at different levels of abstraction. It is able to do so in continuous and noisy environments by 1) an active strategy learning module that uses reinforcement learning and 2) a dynamically adapting skill module that proactively explores the robot's own action capabilities and thereby providing actions to the strategy module. We present results that show the feasibility of simultaneously learning low-level skills and high-level strategies in order to reach a goal while reacting to disturbances like hardware damages. Thereby, the robot drastically increases its overall autonomy.