RAP Vol:强大的对手种群与体积多样性测量
IEEE transactions on neural networks and learning systems
|October 3, 2023
概括
本研究介绍了强大的对手群体与体积多样性测量 (RAP Vol) 以提高深度强化学习 (DRL) 的强度. RAP Vol通过更新所有对手和测量人口多样性来提高数据效率和概括性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 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 (DRL) 在许多领域都表现出色,但与环境变化作斗争,导致不良的概括和现实世界的应用失败.
- 现有的方法,如通过敌对人群 (RAP) 提高稳健性,但数据效率低,人口多样性有限.
研究的目的:
- 提出强大的敌对群体与体积多样性测量 (RAP Vol) 以提高DRL的概括性和对环境动态的强度.
- 在对抗性培训框架中提高数据利用效率和人口多样性.
主要方法:
- 开发了一个共同的对抗性训练框架,其中训练数据更新了所有对手,提高了数据利用率和融合速度.
- 实施了人口多样性的代改进机制,使用对手代表的高维区域的体积来测量和提高多样性.
主要成果:
- 废弃实验证实RAP Vol显著提高了对环境变化的强度.
- 研究了敌对群体规模和多样性对整体强度的影响.
结论:
- RAP Vol有效地解决了以前方法的局限性,在DRL中提供了更高的概括性和稳定性.
- 拟议的方法提高了DRL在动态和不可预测的现实场景中的适用性.
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