Convergence for Natural Policy Gradient on Infinite-State Average-Reward Markov Decision Processes

Infinite-state Markov Decision Processes (MDPs) are essential in modeling and optimizing a wide variety of engineering problems. 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) algorithm. Convergence results for these RL algorithms rest on convergence results for the NPG algorithm. However, all existing results on the …

algorithms context convergence cs.lg decision engineering gradient learn markov modeling natural policy policy-gradient popular processes reinforcement reinforcement learning state