可转换的高斯奖励函数用于使用深度强化学习的社会意识导航
Jinyeob Kim1, Sumin Kang2, Sungwoo Yang2
1Department of Artificial Intelligence, College of Software, Kyung Hee University, Yongin 17104, Republic of Korea.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究引入了可转换的高斯奖励函数 (TGRF),以改善机器人在拥挤地区的导航. TGRF简化了奖励函数的设计,并提高了社会意识的导航系统的学习速度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 机器人导航正在向人类机器人互动的社会意识战略发展.
- 强化学习 (RL) 正在推动社会意识的导航,但奖励函数设计具有挑战性,特别是在拥挤的环境中.
- 目前手动设计的奖励函数存在超参数问题和对象特征的表现不佳.
研究的目的:
- 为了应对设计奖励功能的挑战,为社会意识的机器人导航.
- 引入一种新的,可适应的奖励功能,简化调整并提高性能.
- 在以人为中心的机器人应用中提高深度强化学习 (DRL) 的效率和有效性.
主要方法:
- 开发了一个可转换的高斯奖励函数 (TGRF),具有独立运行的超参数.
- 设计的TGRF具有适应性,允许各种奖励功能应用.
- 机器人导航任务的深度强化学习 (DRL) 框架内集成的TGRF.
主要成果:
- TGRF显著降低了奖励功能调整的复杂性.
- 由于TGRF的可变性,可以在各种导航场景中灵活应用.
- 实验和模拟显示了高性能和加速的学习率使用TGRF在DRL.
结论:
- 对于社会意识的机器人导航,TGRF提供了一种更高效,更有效的方法来奖励功能设计.
- 拟议的方法简化了可以安全有效地与人类一起导航的机器人的开发.
- 在复杂的,动态的环境中,TGRF对推进DRL能力的表现非常有希望.
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