人类脑电图和TD3深度强化学习之间的共享自主性:多代理副驾驶员方法
Chun-Ren Phang1,2,3, Akimasa Hirata1,2
1Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan.
Annals of the New York Academy of Sciences
|March 30, 2025
概括
这项研究整合了自主系统的深度强化学习 (RL) 和脑计算机接口 (BCI). 新型副驾驶员控制方案提高了复杂环境中的人类干预和BCI性能.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 深度强化学习 (RL) 能够实现自主代理.
- 大脑-计算机接口 (BCI) 解码人类大脑的信号.
- 整合RL和BCI可以提高自主系统性能和人类干预.
研究的目的:
- 提出深度RL和BCI之间一种新的集成技术.
- 加强人类对自主系统的干预.
- 通过考虑环境因素来提高BCI的绩效.
主要方法:
- 开发了一个副驾驶控制方案 (Co-FB),在人类 (EEG) 和RL (TD3) 代理之间共享自主权.
- 利用电脑电图 (EEG) 来解码人类行动指令.
- 雇佣双胞胎延迟深决定性政策梯度 (TD3) 对于RL代理行动.
- 引入一个差异指数来评估相互冲突的代理决策.
主要成果:
- Co-FB模型显著超过了个人EEG (EEG-NB) 和TD3控制.
- Co-FB实现了更高的目标接近得分,更低的失败率,并减少了人力工作量.
- 将控制权转移到TD3代理,在不理想的BCI解码过程中提高了性能.
结论:
- 副驾驶系统有效地管理复杂的环境.
- 整合环境因素可以提高BCI的业绩.
- 这种方法可以在自主系统中改善人类-人工智能协作.
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