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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