A Unified Analysis of Importance-Corrected Momentum Actor-Critic over a Markovian Trajectory

Yanjie Dong, Zhou Su, Wei Wang, Gang Wang

We present a generic actor-critic method that combines a ridge-regularized pathwise least-squares temporal-difference (LSTD) critic with importance-corrected momentum (ICM) actors. The critic solves a ridge-regularized system per frame by Newton-Schulz inversion and needs no stepsize, no knowledge of the mixing time, and no rank condition. Averaging stochastic policy gradients over frames of $T$ consecutive transitions, the ICM actors incur a frame-dependent gradient bias of order $\frac{1}{T}$. To our knowledge, we establish the first finite-time bounds for ICM actor-critic methods with normalized and unnormalized steps along a single Markovian trajectory. Up to a stationary-to-discounted distribution mismatch, the averaged gradient norm over $K$ updates is bounded by ${\cal O}(K^{-1/3}+\frac{1}{T})$ for normalized steps and by ${\cal O}(K^{-1/4}+\frac{1}{T})$ for unnormalized steps. We specialize these bounds to Muon, normalized stochastic gradient descent (SGD) with momentum, SGD with momentum, and AdaNorm, and compare them in the nuclear norm. With a dimension-free stepsize, Muon confines the factor $\min\{m,n\}^{1/2}$ associated with an $m\times n$ actor parameter matrix to…