Masking and Demasking Agents
Reinforcement
Observational Learning
Multi-input and Multi-variable systems
Collisions in Multiple Dimensions: Problem Solving
Avoidance Learning and Learned Helplessness
您也可能阅读
通过共同作者、期刊和引用图与本文相关的文章。
Philip Kwaku Adjei1, Qin Zhiguang1, Isaac Amankona Obiri2
1School of Information and Software Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China.
本研究介绍了一种新的多代理强化学习 (MARL) 方法,使用层次图表注意网络 (HGAT) 来检测智能合约的漏洞. 该方法显著提高了识别区块链平台上的各种威胁的准确性.
科学领域:
背景情况:
研究的目的:
主要方法:
主要成果:
结论: