Newest 'alphago' Questions - Artificial Intelligence Stack Exchange
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 ...
Question about AlphaGo Zero's Neural network architecture?
Sep 14, 2020 · 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 ...
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?
What is the significance of move 37? (to a non go player)
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.
How does Alpha Go Zero MCTS work in parallel?
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).
Difference between AlphaGo's policy network and value network
Mar 29, 2016 · 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).
deepmind - Why did AlphaGo lose its Go game? - Artificial …
Basically, AlphaGo is good at making lots of small decisions well, and managing risk and uncertainty better than humans can. One of the things that's surprising about it relative to previous bots that play Go is how good it was at tactical fights; in previous games, Lee had built a position that AlphaGo needed to attack, and then AlphaGo ...
Why AlphaGo didn't use Deep Q-Learning?
Apr 29, 2020 · 9. +50. Q Q -learning (and also its deep variant, and most of the other well-known reinforcement learning algorithms) are inherently learning approaches for single-agent environments. The entire problem setting that these algorithms are developed for (Markov decision processes, or MDPs) is always framed in terms of a single agent situated in ...
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 ...
Does AlphaGo play random moves in a real competition?
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).