Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report
This work addresses energy-efficient status updates in IoT networks, but it appears incremental as it combines existing RL methods for a specific application.
The paper tackles the problem of balancing information freshness and energy consumption in IoT networks by optimizing sensor activation, achieving a solution for continuing MDPs with exponential state-action growth. It integrates R-learning with deep reinforcement learning to maximize long-term average reward, addressing the curse of dimensionality in such scenarios.
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.