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In this paper, we consider an infinite horizon average reward Markov DecisionProcess (MDP). Distinguishing itself from existing works within this context,our approach harnesses the power of the general policy gradient-basedalgorithm, liberating it from the constraints of assuming a linear MDPstructure. We propose a policy gradient-based algorithm and show its globalconvergence property. We then prove that the proposed algorithm has$\tilde{\mathcal{O}}({T}^{3/4})$ regret. Remarkably, this paper marks apioneering effort by presenting the first exploration into regret-boundcomputation for the general parameterized policy gradient algorithm in thecontext of average reward scenarios.

Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes