对体内强化学习代理的奖励函数的发现
Renzhi Lu1, Zonghe Shao1, Yuemin Ding2,3
1School of Artificial Intelligence and Automation, Engineering Research Center of Autonomous Intelligent Unmanned Systems, Chinese Ministry of Education, State Key Laboratory of Digital Manufacturing Equipment and Technology, Institute of Artificial Intelligence, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, 430074, Hubei, China.
Nature communications
|December 11, 2025
概括
本研究引入了一种新的双级优化框架,用于自动发现体现强化学习 (RL) 代理的最佳奖励函数. 这种方法增强了适应能力,并加速了复杂环境中的学习.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 神经科学是一个神经科学.
背景情况:
- 奖励最大化对于生存,进化和认知能力至关重要,例如生物生物体和体内代理人的学习.
- 强化学习 (RL) 显示出在体内代理人中智能决策的前景,但面临着复杂,不确定的现实任务的挑战.
- 目前对嵌入式RL代理的奖励函数设计需要人工工程,领域专业知识和广泛的调整,从而导致效率低下和潜在的失败.
研究的目的:
- 引入一个双层优化框架,用于发现体现强化学习代理的最佳奖励函数.
- 在复杂和不确定的环境中解决传统的,手动设计的奖励信号的局限性.
- 加速政策优化,提高RL代理人在各种任务中体现的适应性.
主要方法:
- 开发了一个双层优化框架.
- 利用遗憾最小化来发现最佳的奖励功能.
- 将框架应用于体现的强化学习代理.
主要成果:
- 拟议的框架加快了对体现的RL代理人的政策优化.
- 这种方法提高了RL代理人在各种任务中的适应性.
- 证明了发现最佳奖励函数的有效机制,克服了手动工程的局限性.
结论:
- 双层优化框架为设计在嵌入式RL中的有效奖励函数提供了一个有希望的解决方案.
- 这种方法可以在复杂的现实场景中显著提高体内代理的效率和适应性.
- 这些发现支持在各种科学学科中更广泛地采用体现RL,并推动对通用人工智能的追求.
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