| 大会名称 |
| 2023年 ソサイエティ大会 |
| 大会コ－ド |
| 2023S |
| 開催年 |
|
2023
|
| 発行日 |
|
2023/9/5
|
| セッション番号 |
|
B-5A
|
| セッション名 |
|
無線通信システムA
|
| 講演日 |
|
2023/9/12
|
| 講演場所(会議室等) |
|
全学教育棟 本館 南棟 2階S2Y講義室
|
| 講演番号 |
|
B-5-2
|
| タイトル |
|
Optimized Channel Selection in Vehicular Systems Using Reinforcement Learning Techniques
|
| 著者名 |
|
◎Yun LI, Yuyuan CHANG, Kazuhiko FUKAWA,
|
| キーワード |
|
Autonomous driving, wireless communications, V2X, cognitive radio, multi-action reinforcement learning
|
| 抄録 |
|
This study presents a solution to spectrum scarcity in cognitive radio-based V2X communications, which have frequently been compounded by oversimplified V2X models. By utilizing a more precise 3D autonomous driving testbed [1] and a proximal policy optimization (PPO) reinforcement learning (RL) algorithm, we propose and apply a multi-action PPO (MA-PPO) to the complex optimization problems. Computer simulations demonstrate that MA-PPO is superior to the con- ventional multi-action Deep Q-network (MA-DQN) approach in terms of both the stability and data transmission efficiency.
|
| 本文pdf |
|
PDF download
|
