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As far as I understand, AlphaZero had three main components: 1. A model of how Go worked, constructed by humans 2. A neural net for generating interesting mov
by gwd 6y ago
As far as I understand, AlphaZero had three main components:
1. A model of how Go worked, constructed by humans
2. A neural net for generating interesting moves, and a neural net for evaluating game positions.
3. A Markov search algorithm that used 1+2 to do pattern patching and search.
I will say that so far, #1 -- constructing mental models which can then be searched -- is something I haven't seen an AI do yet.
But looking at the actual work that I do -- from coding to deciding what to cook for dinner to deciding how to lay out my extension -- I'm pretty sure that behind the scenes it's exactly the same as 1+2+3 above; and that if I could download my mental model and my utility evaluator into a program, it could (with training) do all of the above faster and more consistently.
- RiversHaveWings 6y agoAfter AlphaZero, DeepMind went on to develop MuZero (https://en.wikipedia.org/wiki/MuZero https://en.wikipedia.org/wiki/MuZero, paper: https://arxiv.org/abs/1911.08265 https://arxiv.org/abs/1911.08265), which is able to learn to play games without being provided an explicit model of their rules. "When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules."