@inproceedings{9beede6bef964df1979c6c5ae6e380a1,
title = "Model-Based Reinforcement Learning for Wireless Channel Access",
abstract = "In this paper, we study a wireless channel access method using model-based reinforcement learning in limited spectral resources. The proposed method maximizes sampling efficiency by learning an environment and using it for actor learning. The environment is a model that considers a dynamic packet queue in a situation where a wireless channel is shared. We show the performance results of the proposed learning algorithm in the considered environment.",
keywords = "actor-critic, model-based reinforcement learning, POMDP, wireless channel access, world model",
author = "Park, \{Jong In\} and Choi, \{Kae Won\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",
year = "2022",
doi = "10.1109/ICTC55196.2022.9952476",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "1393--1395",
booktitle = "ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence",
}