An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Electronics

Mengyu Zhao¹  Huazhi Zhang¹  Fuling Yang¹  Suyu Wang¹  Yan Li¹ 

[1] School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China;

关键词: non-cooperative equilibrium;    interactive self-learning;    generative adversarial;    game evolution;    multi-agent confrontation;   

DOI  :  10.3390/electronics10232977

来源: DOAJ

【 摘 要 】

Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered. An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation evolution. A generative adversarial network (GAN) is designed combining with multi-agent interactive self-learning, and the non-cooperative equilibrium strategy is well adopted within the framework of interactive self-learning, aiming for high evolution efficiency and interest. For assessment, three typical multi-agent confrontation experiments are designed and conducted. The results show that, first, in terms of training speed, the ISGE-NCE produces a training convergence rate of at least 46.3% higher than that of the method without considering interactive self-learning. Second, the evolution rate of the interference and detection agents reaches 60% and 80%, respectively, after training by using our method. In the three different experiment scenarios, compared with the DDPG, our ISGE-NCE method improves the multi-agent evolution effectiveness by 43.4%, 50%, and 20%, respectively, with low training costs. The performances demonstrate the significant superiority of our ISGE-NCE method in swarm intelligence.

【 授权许可】

Unknown