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Given that AlphaGo Zero was trained on several million games of self plays, each game involving hundreds of steps, each step with 1600 MCTS simulations, the tot
by fspeech 9y ago
Given that AlphaGo Zero was trained on several million games of self plays, each game involving hundreds of steps, each step with 1600 MCTS simulations, the total number of board positions it has considered is on the order of trillions. While impressive it pales in comparison to the number of possible board positions of 10^170 (https://en.m.wikipedia.org/wiki/Go_and_mathematics https://en.m.wikipedia.org/wiki/Go_and_mathematics). So its amazing performance tells us that:
1. Possibly the elegant rule of the game cuts down the search space so much that there is a learnable function that gives us optimal MCTS supervision;
2. Or CNN approximates human visual intuition so well, so while Zero has not evaluated so many board positions it has evaluated all the positions that human has ever considered - so it remains possible that a different network could produce different strategies and be better than Zero.