Concentration of Cumulative Reward in Markov Decision Processes
This work addresses theoretical foundations for analyzing reward variability in MDPs, which is incremental but important for reinforcement learning researchers.
The paper investigates concentration properties of cumulative rewards in Markov Decision Processes, providing asymptotic results like the law of large numbers and non-asymptotic bounds such as Azuma-Hoeffding-type inequalities, with implications for policy comparison and regret definitions.
In this paper, we investigate the concentration properties of cumulative rewards in Markov Decision Processes (MDPs), focusing on both asymptotic and non-asymptotic settings. We introduce a unified approach to characterize reward concentration in MDPs, covering both infinite-horizon settings (i.e., average and discounted reward frameworks) and finite-horizon setting. Our asymptotic results include the law of large numbers, the central limit theorem, and the law of iterated logarithms, while our non-asymptotic bounds include Azuma-Hoeffding-type inequalities and a non-asymptotic version of the law of iterated logarithms. Additionally, we explore two key implications of our results. First, we analyze the sample path behavior of the difference in rewards between any two stationary policies. Second, we show that two alternative definitions of regret for learning policies proposed in the literature are rate-equivalent. Our proof techniques rely on a novel martingale decomposition of cumulative rewards, properties of the solution to the policy evaluation fixed-point equation, and both asymptotic and non-asymptotic concentration results for martingale difference sequences.