ADDRESSING SCALABILITY CHALLENGES IN ARTIFICIAL SUPERINTELLIGENCE THROUGH A HIERARCHICAL MULTI-AGENT REINFORCEMENT LEARNING ARCHITECTURE

Abstract

Artificial Super Intelligence (ASI) systems need scalable and efficient decision-making models for 
effective functioning in complex and dynamic environments. However, existing reinforcement 
learning approaches have problems such as high computational complexities, low convergence rates, 
and poor coordination among multiple agents. Thus, this paper proposes a novel Hierarchical Multi
Agent Reinforcement Learning framework coupled with hierarchical multi-agent system 
architecture. The proposed framework utilizes knowledge about the environment to minimize the 
search space and stability in learning. Additionally, the hierarchical architecture improves 
coordination among agents at global, intermediate, and local levels. The system is tested using 
simulation techniques under artificial environment conditions. The proposed method is implemented 
using PYTHON software. The experimental results show that the proposed framework improves 
scalability by 65% to 97%, convergence rate by 30% to 98%, and computational efficiency by 60% 
to 92%. The experimental results validate that the proposed framework improves scalability and 
decision-making performance in intelligent systems. This paper emphasizes the benefits of using 
physics-informed learning coupled with hierarchical multi-agent reinforcement learning for future 
ASI applications.