Research breakthrough · curated entry
TD-Gammon reaches backgammon mastery by self-play
TD-Gammon
- Date
- June 1992
- Category
- Research breakthrough
- Organization / Model
- IBM
- Evidence
- 2 sources · 2 evidence families
Tesauro let a network learn position evaluation through self-play and temporal-difference learning, approaching human champion strength. It showed reinforcement learning could work in complex decision tasks without hand-built features, foreshadowing the AlphaGo route.
Curator's note: Cross-checked against the original paper and the Wikipedia article.
Curated sources · 2
Cite this page
This page is a curated static snapshot and can be cited directly. Preferred format GB/T 7714—2025 (electronic resource [EB/OL]); the other 5 styles open below, together with the machine-readable files.
GB/T 7714—2025 (electronic resource [EB/OL])
AI 纪年. TD-Gammon 用神经网络与强化学习达到西洋双陆棋顶尖水平[EB/OL]. (1992-06)[2026-11-03]. https://aihist.net/e/td-gammon-1992.html.
APA 7 · American Psychological Association
AI 纪年. (1992, June). TD-Gammon 用神经网络与强化学习达到西洋双陆棋顶尖水平 [Web page]. https://aihist.net/e/td-gammon-1992.html
MLA 9 · Modern Language Association
"TD-Gammon 用神经网络与强化学习达到西洋双陆棋顶尖水平." AI 纪年, June 1992, aihist.net/e/td-gammon-1992.html. Accessed 3 Nov. 2026.
Chicago 17 · notes-bibliography
AI 纪年. "TD-Gammon 用神经网络与强化学习达到西洋双陆棋顶尖水平." Published June 1992. Accessed Nov. 3, 2026. https://aihist.net/e/td-gammon-1992.html.
IEEE · citation-reference list
"TD-Gammon 用神经网络与强化学习达到西洋双陆棋顶尖水平," AI 纪年, June 1992. [Online]. Available: https://aihist.net/e/td-gammon-1992.html. [Accessed: 3 Nov. 2026].
ACM · author-date variant
AI 纪年. June 1992. TD-Gammon 用神经网络与强化学习达到西洋双陆棋顶尖水平. Retrieved November 3, 2026 from https://aihist.net/e/td-gammon-1992.html.
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