This paper explores ways to discover strategy from a state-action-state-reward log recorded during a reinforcement learning session. The resulting state space weight can also get a higher value than the GA (MVPA value is 249 million, while the GA value is 68 million). The MVPA algorithm is also faster in finding solutions. The experiment results show that GAs and MVPA are very effective in optimizing the state space in the Tetris game. The optimization means in this research is to find the best weight in the state space with the minimum possible training time to play Tetris with the highest possible value. Seeing this, in this study, apply the Genetic Algorithms (GA) and the most valuable player (MVPA) algorithm to optimize state-space training so that artificial intelligence (agents) can play like an expert. It takes a long to train artificial intelligence to play like a Tetris game expert. Starting from applying state-space to reinforcement learning, one of the biggest obstacles of these studies is time. These various studies have been carried out. This is also what makes Tetris often used as research material, especially research in artificial intelligence. Although it seems simple, this game requires strategy and continuous practice to get the best score. Tetris is one of those games that looks simple and easy to play. We show via a reduction to the Multiphase problem [P\u$ factor. In other words, we want a data structure which maintains a set of $O(n)$ rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. To do so, we want a data structure which can always suggest a greedy move. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. So we do that.Consider a variant of Tetris played on a board of width $w$ and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Then we took that, and we reflected it across the y axis. So we went from minus one for two, for one. We swap positions of the coordinates and we took the X coordinate and swap the sign. So we ended up with is we started with a point we wrote to that 0.90 degrees. It was here and reflected across the Y axis, and it's going to move toe over to the left side of the negative side. Remember, for reflection across the y axis we're gonna take, um and we're just gonna change the sign of the X coordinate. And now the next step is reflected across the Y axis. Where is it gonna end up? It starts ad minus one. And so that's just Ah, it's pretty easy if we think about taking this point and rotating it 90 degrees. And when we rotate this about that origin 90 degrees where we end up with is a new point k and minus h. Okay, so remember, uh, uh, rotated point around the origin 90 degrees. So 1st 1 rotate 90 degrees, and then we want to reflect over the Y axis. And we're being asked to rotate that 90 degrees about the origin and then reflected across the y axes. And label that point minus one comma four. Hello There were given a point in this problem of minus one for good.
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