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AlphaGo was introduced in the paper "Mastering the game of Go with deep neural networks and tree search" (2016) by David Silver et al. There have been three more powerful successors of AlphaGo: AlphaGo Master, AlphaGo Zero and AlphaZero.
Apr 29, 2020 · $\begingroup$ @FıratKıyak Actually, even TD-Gammon's algorithm probably "shouldn't" have worked in theory, at least in the sense that such an algorithm was not guaranteed to converge to a correct solution for a 2-player game, even given infinite computation time and an infinitely large neural network.
Feb 26, 2023 · I have seen (and googled) information for Game 2, Move 37 in the AlphaGo vs. Lee Sedol match. However it is difficult to find information concerning this move that doesn't rely on an understanding of go (which I don't have) I would like to understand the significance of this without it being a go gameplay answer.
The earlier AlphaGo version had 4 separate networks, 3 variations of policy network - used during play at different stages of planning - and one value network. Is designed around self-play Uses Monte Carlo Tree Search (MCTS) as part of estimating returns - MCTS is a planning algorithm critical to AlphaZero's success, and there is no equivalent component in DQN
我们最近几个月参照AlphaGo Zero论文复现了，PhoenixGo（野狐账号BensonDarr等） 上个月在野狐围棋上与职业棋手对弈创造了200连胜的纪录，并且取得了在福州举办的2018世界人工智能围棋大赛的冠军
Jun 8, 2020 · AlphaGo Zero only uses the black and white stones from the Go board as its input, whereas previous versions of AlphaGo included a small number of hand-engineered features. What exactly is the input to AlphaGo's neural network? What do they mean by "just white and black stones as input"? What kind of information is the neural network using?
Sep 25, 2023 · To understand how AlphaGo Zero performs parallel simulations think of each simulation as a separate agent that interacts with the search tree. Each agent starts from the root node and selects an action according to the statistics in the tree, such as: (1) mean action value (Q), (2) visit count (N), and; (3) prior probability (P).
$\begingroup$ Also, we knew that AlphaGo was better at playing White than it is at playing Black. This is why Lee suggested (and Deepmind agreed to) Lee playing Black on the last game, rather than the coinflip that it was originally planned to be; Lee wanted to see if the same strategy that worked against AlphaGo's weaker side also worked against its stronger side.
Feb 1, 2016 · And: Isaac Pei's answer to What programming language did Google use to create AlphaGo? But note that David Silver, that Pei links to at "chessprogramming", and while that site covers more than chess, chess uses different algorithms.
May 8, 2020 · To describe how it is used, lets first describe the steps of AlphaGo Zero as a whole. There are 4 "phases" to the Monte-Carlo tree search in AlphaGo Zero as depicted in Figure 2. The first 3 expand and update the tree and together are the "search" in Monte-Carlo tree "search" in AlphaGo Zero.
