4 ms·
I think the fact that it's no longer using Monte Carlo tree search is a huge step forward in the generalizability of the technique. But go is still - a perfect
by habitue 9y ago
I think the fact that it's no longer using Monte Carlo tree search is a huge step forward in the generalizability of the technique. But go is still
- a perfect information game
- with a relatively small input size (vs. arbitrary computer vision)
- cheap to simulate
- discrete action space
- deterministic
This isn't to take away from the magnitude of the achievement, but the nature of the problem itself makes the result less applicable to many tasks we might want to use RL for.
- abecedarius 9y agoMath research shares those qualities, except small input size if you include the body of all the already-known theorems as an input. I don't know if we'll see much smarter proof assistants soon, but it doesn't seem absurd to me as a possible development.
- dane-pgp 9y agoActually, just converting all the already-known theorems into a form that can be computationally verified (not just convince a skilled human) would be an interesting starting point. This would really help the metamath project, and perhaps make peer review of mathematical research papers easier: http://us.metamath.org/mpeuni/mmset.html http://us.metamath.org/mpeuni/mmset.html
- Radim 9y agoExcept it is still using MCTS (heavily).
- shmageggy 9y agoIt still uses MCTS as its search algorithm. It no longer uses random rollouts as part of the evaluation, though. (Previously it was rollouts/2 + value_network/2)
- espadrine 9y agoRandom rollouts are what the MC in MCTS stands for. Without that, it is simply a tree search. Excerpt from the paper: > [AlphaGo Zero] uses a simpler tree search that relies upon this single neural network to evaluate positions and sample moves, without performing any Monte-Carlo rollouts.
- xoroshiro 9y agoDoes this mean it learns what to search? I wonder why they thought it was a good idea. I thought the whole point of MC was that pruning algorithms like the ones in chess wouldn't work for a larger search space.
- shmageggy 9y agoThat's what the policy is. Given a board state, the policy gives you a distribution over all available moves.
- letlambda 9y agoThe policy network is a function from board states to a scoring of moves. The policy network with the greedy heuristic, ie pick the highest rated move with no explicit look ahead method, plays at a high amateur level. This was... unexpectedly good. It effectively reduces the branching factor of Go from the number of moves available, to the number of moves actually worth considering.
- pinouchon 9y agoAre you sure the new version is not using MCTS ? I saw it mentioned in the new paper