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framework, termed \emph{f-Divergence Reinforcement Learning (FRL)}. In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the f-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to