PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Reinforcement Learning for Motor Control
Marc Deisenroth and Carl Edward Rasmussen
In: 10th International PhD Workshop on Systems and Control, 22-26 September 2009, Czech Republic.


Artificial learners often require many more trials than humans or animals when learning motor control tasks in the absence of expert knowledge. We implement two key ingredients of biological learning systems, generalization and incorporation of uncertainty into the decision-making process, to speed up artificial learning. We present a coherent and fully Bayesian framework that allows for efficient artificial learning in the absence of expert knowledge. The success of our learning framework is demonstrated on challenging nonlinear control problems in simulation and in hardware.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5478
Deposited By:Marc Deisenroth
Deposited On:16 October 2009