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	Department of Engineering Computational and Biological Learning Lab

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MP Deisenroth and CE Rasmussen
Bayesian Inference for Efficient Learning in Control
in Multidisciplinary Symposium on Reinforcement Learning, June 2009, Montreal, Canada.

In contrast to humans or animals, artificial learners often require more trials when learning motor control tasks solely based on experience. Efficient autonomous learners will reduce the amount of engineering required to solve control problems. By using probabilistic forward models, we can employ two key ingredients of biological learning systems to speed up artificial learning. We present a consistent and coherent Bayesian framework that allows for efficient autonomous experience-based learning. We demonstrate the success of our learning algorithm by applying it to challenging nonlinear control problems in simulation and in hardware.

MPI for Biological Cybernetics, Tübingen, Germany