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arXiv · 1907.06246

On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost

Zhuoran Yang·Yongxin Chen·Mingyi Hong·Zhaoran Wang
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Abstract

Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind. In a broader context, actor-critic can be viewed as an online alternating update algorithm for bilevel optimization, whose convergence is known to be fragile. To understand the instability of actor-critic, we focus on its application to linear quadratic regulators, a simple yet fundamental setting of reinforcement learning. We establish a nonasymptotic convergence analysis of actor-critic in this setting. In particular, we prove that actor-critic finds a globally optimal pair of actor (policy) and critic (action-value function) at a linear rate of convergence. Our analysis may serve as a preliminary step towards a complete theoretical understanding of bilevel optimization with nonconvex subproblems, which is NP-hard in the worst case and is often solved using heuristics.

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Zhuoran Yang, Yongxin Chen, Mingyi Hong, Zhaoran Wang. 2019-07-14. On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost. https://arxiv.org/abs/1907.06246

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