Web: http://arxiv.org/abs/2208.02362

Sept. 22, 2022, 1:12 a.m. | Samarth Gupta, Daniel N. Hill, Lexing Ying, Inderjit Dhillon

cs.LG updates on arXiv.org arxiv.org

In most applications of model-based Markov decision processes, the parameters
for the unknown underlying model are often estimated from the empirical data.
Due to noise, the policy learnedfrom the estimated model is often far from the
optimal policy of the underlying model. When applied to the environment of the
underlying model, the learned policy results in suboptimal performance, thus
calling for solutions with better generalization performance. In this work we
take a Bayesian perspective and regularize the objective function of …

arxiv bayesian regularization

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