2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSS-3.3
Paper Title An Actor-Critic Reinforcement Learning Approach to Minimum Age of Information Scheduling in Energy Harvesting Networks
Authors Shiyang Leng, The Pennsylvania State University, United States; Aylin Yener, The Ohio State University, United States
SessionSS-3: Machine Learning in Wireless Networks
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Special Sessions: Machine Learning in Wireless Networks
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract We study age of information (AoI) minimization in a network consisting of energy harvesting transmitters that are scheduled to send status updates to their intended receivers. We consider the user scheduling problem over a communication session. To solve online user scheduling with causal knowledge of the system state, we formulate an infinite-state Markov decision problem and adopt model-free on-policy deep reinforcement learning (DRL), where the actor-critic algorithm with deep neural network function approximation is implemented. Comparable AoI to the offline optimal is demonstrated, verifying the efficacy of learning for AoI-focused scheduling and resource allocation problems in wireless networks.