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Multi-Agent Reinforcement Learning (MARL) is revolutionizing real-time system optimization in artificial intelligence. This innovative approach allows multiple autonomous agents to collaborate and adapt in dynamic environments, significantly outperforming traditional single-agent systems. The research emphasizes the potential of MARL to tackle complex optimization challenges across various industries.
MARL's architecture supports distributed decision-making, enhancing scalability and efficiency in interconnected systems. Effective communication among agents is crucial for optimizing performance, while overcoming implementation challenges like learning stability and goal conflicts is essential for maintaining system integrity. The practical applications of MARL in smart city traffic management and food delivery systems illustrate its transformative impact on real-world scenarios.
• MARL enables real-time optimization through collaborative intelligence among autonomous agents.
• Effective communication protocols are essential for optimizing performance in MARL systems.
MARL allows multiple agents to learn and adapt simultaneously in shared environments.
This framework enables agents to make decisions collaboratively, enhancing system efficiency.
These protocols facilitate information exchange among agents, crucial for maintaining system stability.
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