Mastering the game of Go with deep neural networks and tree search (AlphaGo)
圍棋遊戲因其巨大的搜索空間以及評估棋盤位置和動作的難度，一直被視爲人工智能經典遊戲中最具挑戰性的遊戲。本文使用value network評估棋盤位置，並使用policy network選擇行爲。這些深度神經網絡是通過人類專家遊戲中的有監督學習和self-play中的強化學習的新穎組合來訓練的。同時，本文還提出一種新的MCTS搜索算法，該算法將蒙特卡洛模擬與價值和策略網絡相結合。 文章目錄 Intr
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- 1. Mastering the game of Go with deep neural networks and tree search 概括
- 2. 《master the game of GO wtth deep neural networks and tree search》研究解讀
- 3. AlphaGo是如何煉成的：解讀論文 "Mastering the game of Go with deep neural networks and tree search"
- 4. Mastering the game of Go without human knowledge譯文
- 5. Mastering the game of Go without human knowledge (AlphaGo Zero)
- 6. AlphaGo論文的譯文，用深度神經網絡和樹搜索征服圍棋：Mastering the game of Go with deep neural networks and tree search
- 7. 【論文翻譯】Mastering the game of Go with deep neural networks and tree search( 用深度神經網絡和樹搜索實現圍棋遊戲)
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相關文章
- 1. Mastering the game of Go with deep neural networks and tree search 概括
- 2. 《master the game of GO wtth deep neural networks and tree search》研究解讀
- 3. AlphaGo是如何煉成的：解讀論文 "Mastering the game of Go with deep neural networks and tree search"
- 4. Mastering the game of Go without human knowledge譯文
- 5. Mastering the game of Go without human knowledge (AlphaGo Zero)
- 6. AlphaGo論文的譯文，用深度神經網絡和樹搜索征服圍棋：Mastering the game of Go with deep neural networks and tree search
- 7. 【論文翻譯】Mastering the game of Go with deep neural networks and tree search( 用深度神經網絡和樹搜索實現圍棋遊戲)
- 8. AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search中文譯文
- 9. Exploring the teaching of deep learning in neural networks
- 10. On the Number of Linear Regions of Deep Neural Networks