A Novel Multi-Agent Reinforcement Learning Framework for Autonomous Decision-Making
Abstract
Multi-agent reinforcement learning (MARL) has gained attention for its applications in robotics, finance, and autonomous systems. This paper introduces a novel MARL framework that enhances coordination among agents using hierarchical learning strategies. We compare our approach with traditional single-agent and independent MARL techniques in simulated environments, including traffic management and robotic swarm control. Experimental results show that our proposed model improves learning efficiency and decision-making accuracy in complex, multi-agent scenarios.
References
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