New algorithm makes it easier for computers to solve decision making problems
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Decision making problems involving multiple agents are often solved using policy iteration, in which a given policy generates a better one. However, the required amount of computation is often prohibitively large. In a new study, a computer scientist takes things to the next level with an approach where decisions are made sequentially and the computations grow only linearly with agents, drastically reducing its complexity and opening doors to a revolution in studying multiagent systems.
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