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The Development of the TD-Gammon Neural Network (1992)

    Gerald Tesauro at IBM developed TD-Gammon, a neural network that learned to play backgammon at a world-class human level using temporal-difference reinforcement learning. Because it trained entirely by playing millions of games against itself, it proved that a reinforcement learning agent could discover complex strategies beyond human expertise without explicit symbolic instructions.

    Part of the 30 AI Roots Facts: The Dawn of Internet & Statistical Foundations (1990–1995) archive. HistoricallyVerified

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