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Bookmark or Share- 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 ...
- How does Alpha Go Zero MCTS work in parallel?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).
- deep learning - What is the input to AlphaGo's neural network ...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?
- deepmind - Why did AlphaGo lose its Go game? - Artificial Intelligence ...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 ...
- Newest 'alphago' Questions - Artificial Intelligence Stack ExchangeAlphaGo 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.
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