Convergence of Natural Policy Gradient for a Family of Infinite-State Queueing MDPs
This provides the first convergence rate bound for NPG in infinite-state average-reward MDPs, addressing a theoretical gap for researchers in reinforcement learning and queueing theory.
The paper tackles the lack of convergence guarantees for the Natural Policy Gradient (NPG) algorithm in infinite-state settings, proving an O(1/√T) convergence rate for a class of queueing MDPs when initialized with the MaxWeight policy.
A wide variety of queueing systems can be naturally modeled as infinite-state Markov Decision Processes (MDPs). In the reinforcement learning (RL) context, a variety of algorithms have been developed to learn and optimize these MDPs. At the heart of many popular policy-gradient based learning algorithms, such as natural actor-critic, TRPO, and PPO, lies the Natural Policy Gradient (NPG) policy optimization algorithm. Convergence results for these RL algorithms rest on convergence results for the NPG algorithm. However, all existing results on the convergence of the NPG algorithm are limited to finite-state settings. We study a general class of queueing MDPs, and prove a $O(1/\sqrt{T})$ convergence rate for the NPG algorithm, if the NPG algorithm is initialized with the MaxWeight policy. This is the first convergence rate bound for the NPG algorithm for a general class of infinite-state average-reward MDPs. Moreover, our result applies to a beyond the queueing setting to any countably-infinite MDP satisfying certain mild structural assumptions, given a sufficiently good initial policy. Key to our result are state-dependent bounds on the relative value function achieved by the iterate policies of the NPG algorithm.