On Gaussian approximation for entropy-regularized Q-learning with function approximation
In this paper, we derive rates of convergence in the high-dimensional central limit theorem for Polyak--Ruppert averaged iterates generated by entropy-regularized asynchronous Q-learning with linear function approximation and a polynomial stepsize $k^{-ω}$, $ω\in (1/2,1)$. Assuming that the sequence of observed triples $(s_k,a_k,s_{k+1})_{k \geq 0}$ forms a uniformly geometrically ergodic Markov chain, and under suitable regularity conditions for the projected soft Bellman equation, we establish a Gaussian approximation bound in the convex distance with rate of order $n^{-1/4}$, up to polylogarithmic factors in $n$, where $n$ is the number of samples used by the algorithm. To obtain this ...
| Authors | Artemy Rubtsov, Rahul Singh, Eric Moulines et al. |
|---|---|
| Primary category | stat.ML |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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