On the Convergence of Single-Timescale Actor-Critic

• URL: http://arxiv.org/abs/2410.08868v2
• Date: Wed, 04 Jun 2025 12:47:22 GMT
• Title: On the Convergence of Single-Timescale Actor-Critic
• Authors: Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor,
• Abstract summary: We analyze the global convergence of the single-timescale actor-critic (AC) algorithm for the infinite-horizon discounted Decision Processes (MDs) with finite state spaces.<n>We demonstrate that the step sizes for both the actor and critic must decay as ( O(k-Pfrac12) ) with $k$ diverging from the conventional ( O(k-Pfrac12) ) rates commonly used in (non- optimal) Markov framework optimization.
• Score: 49.19842488693726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textbf{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 \textbf{stationary policy}, which is equivalent to the complexity of $O(\epsilon^{-4})$ to achieve $\epsilon$-close \textbf{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.

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