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不同奖励政策梯度差异奖励政策梯度
Jacopo Castellini1, Sam Devlin2, Frans A Oliehoek3
1Department of Computer Science, University of Liverpool, Liverpool, UK.
Neural computing & applications
|June 30, 2025
概括
通过将差异奖励与政策梯度相结合,Dr.Reinforce为多代理强化学习提供了一个新的解决方案. 这种方法有效地解决了分散政策的多代理信用分配问题,即使奖励函数是未知的.
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:
- 政策梯度方法在多代理强化学习中被广泛使用.
- 一个重大挑战是多代理信用分配,这对于有效的政策学习至关重要.
- 现有的方法往往难以准确评估个体代理的贡献.
研究的目的:
- 提出一种新的算法,Reinforce博士,用于改进多代理信用分配.
- 在多代理强化学习环境中,使分散政策的学习成为可能.
- 为了提供一个解决方案,在奖励函数是已知的和未知的情况下都起作用.
主要方法:
- 强化博士将差异奖励与政策梯度直接结合起来.
- 它避免了学习Q函数的需要,与反事实多代理政策梯度 (COMA) 等方法不同.
- 对于未知的奖励函数,一个辅助奖励网络被训练来估计差异奖励.
主要成果:
- 强化博士有效地解决了多代理信用分配问题.
- 该算法促进了对分散政策的学习.
- 一种变体的Dr.Reinforce证明了有效性,即使奖励功能是不是明确地知道.
结论:
- 博士强化在多代理强化学习方面取得了重大进展.
- 与现有技术相比,该方法提供了一种更直接的信用分配方法.
- Dr.Reinforce 为各种多代理学习场景提供灵活有效的解决方案.
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