Newest 'alphago' Questions - Artificial Intelligence Stack Exchange
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.
Question about AlphaGo Zero's Neural network architecture?
2020年9月14日 · The following text is quoted from the AlphaGo Zero Paper 2017 from Nature. My question is regarding the eight features. The input to the neural network is a 19 × 19 × 17 image stack comprising 17 binary feature planes.
deep learning - What is the input to AlphaGo's neural network ...
2020年6月8日 · 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?
What is the significance of move 37? (to a non go player)
2023年2月26日 · 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.
What is the difference between DQN and AlphaGo Zero?
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 ...
Why AlphaGo didn't use Deep Q-Learning?
$\begingroup$ @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 …
Did Alphago zero actually beat Alphago 100 games to 0?
2020年10月21日 · Did AlphaGo and AlphaGo play 100 repetitions of the same sequence of boards, or were there 100 different games? Background: Alphago was the first superhuman go player, but it had human tuning and training. AlphaGo zero learned to be more superhuman than superhuman. Its supremacy was shown by how it beat AlphaGo perfectly in 100 games.
Difference between AlphaGo's policy network and value network
2016年3月29日 · AlphaGo needs to generate a simulation with Monte-Carlo, and it needs to choose move 1 or 2. Now, AlphaGo draws a uniform-distributed random variable, and it'll pick: Move 1 if the random number is <= 0.9; Move 2 if the random number is > 0.9; So AlphaGo is much more likely to pick the losing move to simulate (in our very first simulation).
AlphaGo (and other game programs using reinforcement …
2016年4月10日 · AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by …
How does Alpha Go Zero MCTS work in parallel?
2023年9月25日 · 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).