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Of Uncertain Value -- Bayesian Quadratic Reinforcement Learning

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If you have a question about this talk, please contact Emli-Mari Nel.

How much do you need to know about the future to take the right decisions?

In this talk, I will briefly review two influential recent works in Bayesian Reinforcement Learning, and then move on to my own work in progress, on an attempt to find a good Gaussian approximation to the Bellman posterior over values. Unfortunately, the “right” Gaussian fit turns out to be very difficult to track, but a simplistic approximation gives surprisingly good results. Preliminary experiments suggest that this approach compares favorably to contemporary Reinforcement Learning algorithms.

This talk is part of the Inference Group series.

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