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James put together a really nice summary of the ideas and the projects! It was almost a year ago that lc0 was launched, since then the community (led by Alexan
by glinscott 8y ago
James put together a really nice summary of the ideas and the projects!
It was almost a year ago that lc0 was launched, since then the community (led by Alexander Lyashuk, author of the current engine) has taken it to a totally different level. Follow along at http://lczero.org http://lczero.org!
Gcp has also done an amazing job with Leela Zero, with a very active community on the Go side. http://zero.sjeng.org http://zero.sjeng.org
Of course, DeepMind really did something amazing with AlphaZero. It’s hard to overstate how dominant minimax search has been in chess. For another approach (MCTS/NN) to even be competitive with 50+ years of research is amazing. And all that without any human knowledge!
Still, Stockfish keeps on improving - Stockfish 10 is significantly stronger than the version AlphaZero played in the paper (no fault of DeepMind; SF just improves quickly). We need a public exhibition match to setttle the score, ideally with some GM commentary :). To complete the links you can watch Stockfish improve here: http://tests.stockfishchess.org http://tests.stockfishchess.org.
- elcomet 8y agoI thought alphazero was based on minimax, and used neural networks to evaluate moves, isn't this the case ?
- 317070 8y agoIt is kind of the case, but it does not need to expand the whole node to find the maximum. It samples some children instead from a NN (the Monte Carlo aspect)
- glinscott 8y agoMCTS is not a traditional depth first minimax framework. Key concepts like alpha-beta don’t apply. Although it is proven to converge to minimax in the limit, the game trees are so large this is not relevant. You could use the network in a minimax searcher, but it’s so much slower than a conventional evaluation function it’s unlikely to be competitive.
- stabbles 8y agoVery roughly you can think of AlphaZero as a best-first tree search, where 'best' is some statistical estimate.
- CJefferson 8y agoI strongly suspect alphazero is easily beatable, once you have your hands on it. This is just from experience that most neural network style systems are weak against adversarial opponents who understand their internals. Of course I can't be sure, because Google refuses to give out anyone access to alphazero, or a network trained with it. Personally, that gives me more confidence they know there are significant exploitable weaknesses.
- StreamBright 8y agoI guess if you understand the internal of a NN you can just write a paper to publish it. Generating the right noise was proven to be successful against NNs (https://blog.openai.com/adversarial-example-research/ https://blog.openai.com/adversarial-example-research/) but I am not sure how could you apply that to this context.
- henrikf 8y agoNo need to wait for AlphaZero, you can try Leela Chess Zero today. From my experience the network without search has some blind spots, but the tree search is pretty effective in fixing them.
- bertil 8y agoAdversarial? If the model exclusively trains against itself, you can’t really insert anything there. Do you mean, play confusing moves at the beginning of the game?
- CJefferson 8y agoI mean, if we had the network, it would be easy to beat, the same way you can confuse image recognition systems with very minor changes.
- IanCal 8y agoThat doesn't follow. For you to confuse it, you need to change the inputs. For images, this is fine, we can smoothly change lots of little things. For chess games or go you don't have that freedom. You can download the weights for LCZero right now though and try out your theory. https://github.com/LeelaChessZero/lc0/wiki/Getting-Started https://github.com/LeelaChessZero/lc0/wiki/Getting-Started