Mastering the game of Go with deep neural networks and tree search (AlphaGo)
围棋游戏因其巨大的搜索空间以及评估棋盘位置和动作的难度，一直被视为人工智能经典游戏中最具挑战性的游戏。本文使用value network评估棋盘位置，并使用policy network选择行为。这些深度神经网络是通过人类专家游戏中的有监督学习和self-play中的强化学习的新颖组合来训练的。同时，本文还提出一种新的MCTS搜索算法，该算法将蒙特卡洛模拟与价值和策略网络相结合。 文章目录 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》研究解读
- 3. AlphaGo是如何炼成的：解读论文 "Mastering the game of Go with deep neural networks and tree search"
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- 7. 【论文翻译】Mastering the game of Go with deep neural networks and tree search( 用深度神经网络和树搜索实现围棋游戏)
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相关文章
- 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》研究解读
- 3. AlphaGo是如何炼成的：解读论文 "Mastering the game of Go with deep neural networks and tree search"
- 4. Mastering the game of Go without human knowledge译文
- 5. Mastering the game of Go without human knowledge (AlphaGo Zero)
- 6. AlphaGo论文的译文，用深度神经网络和树搜索征服围棋：Mastering the game of Go with deep neural networks and tree search
- 7. 【论文翻译】Mastering the game of Go with deep neural networks and tree search( 用深度神经网络和树搜索实现围棋游戏)
- 8. AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search中文译文
- 9. Exploring the teaching of deep learning in neural networks
- 10. On the Number of Linear Regions of Deep Neural Networks