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For questions related to DeepMind's AlphaGo, which is the first computer Go program to beat a human professional Go player without handicaps on a full-sized 19x19 board. 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 ...
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?
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.
Apr 29, 2020 · 6. Deep Q Learning is a model-free algorithm. In the case of Go (and chess for that matter) the model of the game is very simple and deterministic. It's a perfect information game, so it's trivial to predict the next state given your current state and action (this is the model). They take advantage of this with MCTS to speed up training.
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).
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
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.
Jan 19, 2022 · Alphago and AlphaGo zero use random play to generate data and use the data to train DNN. "Random play" means that there is a positive probability for AlphaGo to play some suboptimal moves based on the current DNN; this is for exploring and learning purposes (please correct me if my understanding is wrong).
In the paper Mastering the game of Go with deep neural networks and tree search, the input features of the networks of AlphaGo contains a plane of constant ones and a plane of constant zeros, as following.
Sep 6, 2020 · I've read through the Alpha(Go)Zero paper and there is only one thing I don't understand. The paper on page 1 states: The MCTS search outputs probabilities π of playing each move.
