On the Convergence of Single-Timescale Actor-Critic

We analyze the global convergence of the single-timescale actor-critic (AC) algorithm for the infinite-horizon discounted Markov Decision Processes (MDPs) with finite state spaces. To this end, we introduce an elegant analytical framework for handling complex, coupled recursions inherent in the algorithm. Leveraging this framework, we establish that the algorithm converges to an $\epsilon$-close globally optimal policy with a sample complexity of $O(\epsilon^{-3})$. This significantly improves upon the existing complexity of $O(\epsilon^{-2})$ to achieve $\epsilon$-close stationary policy, which is equivalent to the complexity of $O(\epsilon^{-4})$ to achieve $\epsilon$-close globally optimal policy using gradient domination lemma. Furthermore, we demonstrate that to achieve this improvement, the step sizes for both the actor and critic must decay as $O(k^{-\frac{2}{3}})$ with iteration $k$, diverging from the conventional $O(k^{-\frac{1}{2}})$ rates commonly used in (non)convex optimization.