Multi-Agent Reinforcement Learning (MARL) has gained significant attention in complex decision-making and multi-agent coordination. However, is MARL the only viable approach, or are there alternative frameworks that could surpass it in scalability, efficiency, and adaptability?
I think MARL is the best for design purposes, and there are no alternatives. Imitation can be another viable alternative. Instead of fighting, let them teach one another.
Mean Field game theory could be the better one which could surpass Multi agent reinforcement learning in scalability, efficiency, and adaptability.
Mean-Field Game (MFG) Theory is a mathematical framework designed to model the strategic behavior of a large population of interacting agents. Instead of explicitly considering the actions of every individual agent (as in traditional multi-agent game theory), MFG simplifies the problem by approximating the collective behavior of all agents using a mean-field distribution.
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