Why Generalist AI Algorithms Outperform Specialized Ones in Zero-Sum Games
New research from MIT reveals that general-purpose neural networks, trained with policy gradient methods, can surpass specialized game-theoretic algorithms in zero-sum games with imperfect information. The findings challenge long-held assumptions and introduce a benchmark for fair algorithm comparison.