Abstract:In this article, we address the issues of stability and data-efficiency in reinforcement learning (RL). A novel RL approach, Kullback–Leibler divergence-regularized distributional RL (KLC51) is proposed to integrate the advantages of both stability in the distributional RL and data-efficiency in the Kullback-Leibler (KL) divergence-regularized RL in one framework. KLC51 derived the Bellman equation and the TD errors regularized by KL divergence in a distributional perspective and explored the approximated str… Show more
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