Learning to Control
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If you have a question about this talk, please contact Dr Guy-Bart Stan.
Learning from examples is a powerful paradigm for aquiring motor
skills in biological (human) systems. In this talk I will examine the
possibility of learning control in artificial systems using modern
probabilistic models from machine learning. The focus of the talk will
be the learning process itself, and I will demonstrate very fast
learning from no prior knowledge in a cart and pole balancing
problem. Technically, our approach is based on reinterpreting control
as a problem of statistical inference, and we show that the proper,
Bayesian, treatment of uncertainty is crucial to the success of the
method.
The talk is based on on-going research in collaboration with Marc
P. Deisenroth .
This talk is part of the CUED Control Group Seminars series.
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