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Value Propagation: A Graphical Model for Bayesian Reinforcement Learning

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

I will present Bayesian Reinforcement Learning methods based on the model-free approach used in the Temporal Difference family of algorithms. Our implementations allow for the incorporation of prior knowledge in a principled way and automatically adapt their learning rate and backup depth. In policy iteration settings, they can guide exploration and converge on the optimal policy in an automated fashion, because they track the uncertainty of evaluations.

This talk is part of the Machine Learning Journal Club series.

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Webinar & FW11, Computer Laboratory, William Gates Building. SS03 of the Computer Lab Online (Zoom meeting). For details, please contact events@slcu.cam.ac.uk

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