Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report

TL;DR

This paper develops a deep reinforcement learning framework for optimizing long-term average rewards in continuing Markov Decision Processes, specifically applied to IoT sensor activation for balancing information freshness and energy consumption.

Contribution

It introduces a novel methodology combining R-learning with traditional DRL algorithms to effectively optimize average rewards in high-dimensional continuing MDPs.

Findings

Effective DRL algorithms for maximizing average reward in continuing MDPs.

Application to IoT sensor management balancing freshness and energy.

Overcoming curse of dimensionality in large state-action spaces.

Abstract

Recently, we have struck the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status in caching enabled Internet of Things (IoT) networks [1]. To solve this problem, we cast the corresponding status update procedure as a continuing Markov Decision Process (MDP) (i.e., without termination states), where the number of state-action pairs increases exponentially with respect to the number of considered sensors and users. Moreover, to circumvent the curse of dimensionality, we have established a methodology for designing deep reinforcement learning (DRL) algorithms to maximize (resp. minimize) the average reward (resp. cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one. In this technical report, we would present detailed discussions on the technical contributions of this methodology.