Teaching Deep Convolutional Neural Networks to Play Go
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Re: Teaching Deep Convolutional Neural Networks to Play Go
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Re: Teaching Deep Convolutional Neural Networks to Play Go
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Specialized pattern recognition in extremely constrained environments is only a hair's width away from higher order intelligence, self-awareness, and a desire for independence.
Re: Teaching Deep Convolutional Neural Networks to Play Go
#4[deleted]
Specialized pattern recognition in extremely constrained environments is only a hair's width away from higher order intelligence, self-awareness, and a desire for independence.
Just train it to predict the actions of people with self-awareness and a desire for independence, and presto!
Re: Teaching Deep Convolutional Neural Networks to Play Go
#5For those (like me) wondering how strong Feugo is, I found this: http://www.gokgs.com/graphPage.jsp?user=fuego19
Re: Teaching Deep Convolutional Neural Networks to Play Go
#6It should be pretty neat to see where this goes. I'm particularly curious what it would take to bridge the gap from 4-5 kyu play to dan-level rankings. I believe there are computer go programs that play around 5-dan level (e.g. Zen19D, CrazyStone), using Monte Carlo, of course. Will the techniques in this article be able to scale up to that?
Re: Teaching Deep Convolutional Neural Networks to Play Go
#7It should be pretty neat to see where this goes. I'm particularly curious what it would take to bridge the gap from 4-5 kyu play to dan-level rankings. I believe there are computer go programs that play around 5-dan level (e.g. Zen19D, CrazyStone), using Monte Carlo, of course. Will the techniques in this article be able to scale up to that?
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Re: Teaching Deep Convolutional Neural Networks to Play Go
#8I wish there was something using DNNs that users could train and use without writing code or connecting to some centralized service. It would help to get the "feel" for how good these algorithms are. Something moderately general-purpose, I mean. However, it seems that mapping them to the problem domain is an art in itself.
Re: Teaching Deep Convolutional Neural Networks to Play Go
#9Re: Teaching Deep Convolutional Neural Networks to Play Go
#10I would be extremely interested in seeing this used as a pruning function for a state-of-the-art MCTS (Monte Carlo Tree Search) Go engine.
As it stands, your generic MCTS algorithm expands a game tree of nodes, and gives more attention to more promising branches, but it still must give attention to other branches to find out if they are promising or not (exploration vs. exploitation).
In the paper, they get the right move (right as defined by what an expert human would do) 44% of the time, but they also say the right move, if not the #1 choice, is often in the top few choices. According to their graph it's in the top 10 choices about 80% of the time, and in the top 30 choices about 98% of the time.
If in MCTS you could prune the branching factor of your tree search down from 300+ to ~30, that could be huge.
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I'd also be interested in seeing it used as the playout function of an MCTS engine.
As it stands, most playout functions use random, or random+quick heuristic to playout thousands (or millions) of random games to rate a position. I imagine if you used this, which can output an entire probability distribution of moves, you could do significantly better than random with a fewer number of games.