Optimal control and optimal sampling: A statistical physics perspective.
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If you have a question about this talk, please contact Tim Hughes.
Intelligent systems, whether natural or artificial, must act in a world that is highly unpredictable. To plan actions with uncertainty is a stochastic optimal control problem. However, there are two fundamental problems: the optimal control solution is intractable to compute and intractable to represent due the nontrivial state dependence of the optimal control. This has prevented large scale application of stochastic optimal control theory sofar. The path integral control theory describes a class of control problems whose solution can be computed as an inference computation. In this presentation we formalize the intuitive notion that the efficiency of the inference computation
is related to the proximity of the sampling control to the optimal control. Secondly, we show new results that allow approximate computation
of state dependent optimal controls in terms of basis functions. These two ingredients together suggest a novel adaptive sampling procedure. We illustrate the results on a few examples.
This talk is part of the CUED Control Group Seminars series.
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