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Optimal Reinforcement Learning for Gaussian Systems

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If you have a question about this talk, please contact Carl Edward Rasmussen.

The exploration-exploitation trade-off is among the central challenges of reinforcement learning. The optimal Bayesian solution is intractable in general. In this talk I will show that, however, if all beliefs are Gaussian processes, it is possible to make analytic statements about optimal learning of both rewards and transition dynamics, for nonlinear, time-varying systems in continuous time and space, subject to a relatively weak restriction on the dynamics: The solution is described by an infinite-dimensional partial differential equation. An approximate finite-dimensional projection provides a first impression for how this result may be helpful.

This talk is part of the Machine Learning @ CUED series.

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