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Transfer of Value Functions via Variational Methods
Andrea Tirinzoni · Rafael Rodriguez Sanchez · Marcello Restelli

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #126

We consider the problem of transferring value functions in reinforcement learning. We propose an approach that uses the given source tasks to learn a prior distribution over optimal value functions and provide an efficient variational approximation of the corresponding posterior in a new target task. We show our approach to be general, in the sense that it can be combined with complex parametric function approximators and distribution models, while providing two practical algorithms based on Gaussians and Gaussian mixtures. We theoretically analyze them by deriving a finite-sample analysis and provide a comprehensive empirical evaluation in four different domains.

Author Information

Andrea Tirinzoni (Politecnico di Milano)
Rafael Rodriguez Sanchez (Politecnico di Milano)
Marcello Restelli (Politecnico di Milano)

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