Skip to Main content Skip to Navigation
Conference papers

Bayesian Inference for Least Squares Temporal Difference Regularization

Abstract : This paper proposes a fully Bayesian approach for Least-Squares Temporal Differences (LSTD), resulting in fully probabilistic inference of value functions that avoids the overfitting commonly experienced with classical LSTD when the number of features is larger than the number of samples. Sparse Bayesian learning provides an elegant solution through the introduction of a prior over value function parameters. This gives us the advantages of probabilistic predictions, a sparse model, and good generalisation capabilities, as irrelevant parameters are marginalised out. The algorithm efficiently approximates the posterior distribution through variational inference. We demonstrate the ability of the algorithm in avoiding overfitting experimentally.
Document type :
Conference papers
Complete list of metadata

Cited literature [33 references]  Display  Hide  Download
Contributor : Christos Dimitrakakis Connect in order to contact the contributor
Submitted on : Monday, September 25, 2017 - 8:44:04 PM
Last modification on : Friday, January 7, 2022 - 3:44:01 AM
Long-term archiving on: : Tuesday, December 26, 2017 - 2:26:20 PM


Files produced by the author(s)


  • HAL Id : hal-01593212, version 1


Nikolaos Tziortziotis, Christos Dimitrakakis. Bayesian Inference for Least Squares Temporal Difference Regularization. ECML 2017 - European Conference on Machine Learning, 2017-09-22, Sep 2017, Skopje, Macedonia. ⟨hal-01593212⟩



Les métriques sont temporairement indisponibles