Using alpha-divergences for entropic regularization in MDPs unifies actor-critic architectures via closed-form policy improvement and provides asymptotic analysis on standard RL problems.
Improving predictive inference under covariate shift by weighting the log-likelihood function
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Entropic Regularization of Markov Decision Processes
Using alpha-divergences for entropic regularization in MDPs unifies actor-critic architectures via closed-form policy improvement and provides asymptotic analysis on standard RL problems.