Natural Policy Gradient as Doubly Smoothed Policy Iteration: A Bellman-Operator Framework

In this work, we show that natural policy gradient (NPG), a core algorithm in reinforcement learning, admits an exact interpretation as a smoothed and averaged form of policy iteration. Specifically, we introduce doubly smoothed policy iteration (DSPI), a Bellman-operator framework in which each policy is obtained by applying a regularized greedy step to a weighted average of past $Q$-functions. DSPI includes policy iteration, dual-averaged policy iteration, NPG, and more general policy dual averaging methods as special cases. Using only monotonicity and contraction of smoothed Bellman operators, we prove distribution-free global geometric convergence of DSPI. Consequently, standard NPG and policy dual averaging achieve an iteration complexity of $\mathcal{O}((1-\gamma {)}^{-1}\log ((1-\gamma {)}^{-1}{ϵ}^{-1}))$ for computing an $ϵ$-optimal policy, without modifying the MDP, adding regularization beyond the mirror map inherent in the update, or using adaptive, trajectory-dependent stepsizes. For the unregularized greedy case, we also prove finite termination of dual-averaged policy iteration. The same Bellman-operator framework extends to discounted MDPs with linear function approximation and to stochastic shortest path problems.