In a losing situation though, it's common for every move which gets explored to look worse quickly (as defeat is closer and surer), which at a point will result in wider rather than deeper searches.This area comprises the sandbanks, mudbanks, dunes and freshwater pools and marshes at the mouth of the Sabaki, Kenya’s second-longest river, c.5 km north of Malindi town, between the Malindi–Mambrui road bridge and the sea. This will still also usually be a top move, since moves which are quickly refuted don't continue getting explored for long. So the program will typically choose the move that is best explored, or based on a combination of confidence and winrate, rather than one for which its prior estimate for winrate is best. Its enthusiasm about a move might change as it explores the consequences, but its confidence in how good the move is improves. ![]() The tree search sorts the order in which moves should be explored based on how promising they seem - initially based entirely on the network, but then also on the conclusions of the search as it runs. It uses that single network to evaluate which moves are promising, and does a tree search based upon it. Leela Zero (like AlphaGo Zero) has only one network though, so there's no distinction between how salient a move is and how good it appears to be. But if the policy network thinks F17 is vastly superior to E3 and the value network says it doesn't make much difference, the engine might end up going with the policy network's first choice. Like a human, the engine will generally assign more importance to the judgments of the value network. First, your "policy network" makes an intuitive judgment about which moves are worth looking at, and then you ultimately decide on a move based on the subsequent analysis of your "value network". It's analogous to how a human brain analyzes a Go position. The win percentages assigned by the value network are what you see on the board. The value network looks at the move and resulting variations, then assigns an estimated win percentage to the result. This significantly limits the amount of moves that the value network then needs to analyze. My guess (this is speculation) is that the policy network prefers the green moves to such an extent that the small difference in win % (as judged by the value network) doesn't weigh up to it.Įxplanation: The policy network is the main feature of a neural network and makes "intuitive" guesses as to which moves may be good based on what it has learned in self-play. The algorithm dismissed these quickly as inferior. Whereas the red colored moves have far fewer playouts. So it considers this playable but 2nd tier. Notice that the white colored move has almost as many playouts as the green ones. So AI will prefer a move that is very very likely to give 56-57% chance to win than a move that has a 54-58% chance to win. The reason some moves may show a higher win pct but aren't preferred is that the algorithm has less confidence in them. That is the move it thinks is most promising and had the most confidence in. After a large number of playouts, it will select the move it explored the most. If, in the course of exploring other moves, it decides that that move is actually more promising, it will explore that more more. But will spend some time exploring other moves. ![]() This is the basis for the Monte Carlo Tree Search algorithm that all of these sophisticated go bots use.Įssentially, the algorithm will spend more time exploring moves that look more promising. Playouts are the number listed below the win pct, and it represents the number of partial games the algorithm has played starting from this move. ![]() The AI doesn't pick the move with the highest win pct as the best move.
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