一个由大脑启发的神经网络神经网络理论提高了多个代理合作和竞争
Zhuoya Zhao1,2, Feifei Zhao1, Yuxuan Zhao1
1Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Patterns (New York, N.Y.)
|August 21, 2023
概括
这项研究引入了一种新的多代理心理理论决策 (MAToM-DM) 模型. 整合ToM在多代理强化学习 (MARL) 任务中提高了合作和竞争效率,从而提高了奖励.
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:
- 思想理论 (ToM) 对社会互动至关重要,它可以在合作和竞争场景中预测行为.
- 现有的多代理强化学习 (MARL) 模型缺乏ToM,限制了它们在动态的社会环境中的表现.
研究的目的:
- 提出一种新的多代理 ToM 决策 (MAToM-DM) 模型,将 ToM 机制集成到 MARL 中.
- 通过预测他人的行为来提高代理人的决策能力.
主要方法:
- 开发了一个MATOM-DM模型,包括一个MATOM尖端神经网络 (MAToM-SNN) 和一个决策模块.
- 设计了两个由大脑启发的ToM模块 (Self-MAToM和Other-MAToM) 以基于自我经验和观察来预测代理行为.
- 代理人根据其他代理人的预测行为调整他们的行为.
主要成果:
- 在实验任务中,MATOM-DM模型证明了加强合作和竞争效率.
- 使用ToM机制的代理商与传统的MARL模型相比获得了更高的回报.
- 提出的模型有效地预测和适应他人的行为.
结论:
- 整合思维理论机制显著提高了多代理强化学习的表现.
- MAToM-DM模型为开发更复杂,更有社会意识的AI代理提供了一个有希望的方法.
- 这项研究突出了大脑启发的ToM模块在推进MARL能力方面的潜力.
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