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Playing Gomoku with Neural Networks
 Slávka, Michal ; Kolář, Martin (oponent) ; Hradiš, Michal (vedoucí práce)
This thesis explores the usage of AlphaZero algorithm for the game of Gomoku. AlphaZero is a reinforcement learning algorithm, which does not require any existing datasets and is able to improve only by using self-play. It uses a tree search for policy improvement, which is subsequently used for training. This approach was able to defeat the previous state of the art methods. Generating training data of high quality requires a lot of computationally expensive iterations, which makes them algorithm slow to train. Experiments show that the strength of the play is growing with each subsequent iteration, this might indicate that it still has room for improvement with more training and that it has not reached its full potential.
Úplný záznam
Playing Gomoku with Neural Networks
 Slávka, Michal ; Kolář, Martin (oponent) ; Hradiš, Michal (vedoucí práce)
This thesis explores the usage of AlphaZero algorithm for the game of Gomoku. AlphaZero is a reinforcement learning algorithm, which does not require any existing datasets and is able to improve only by using self-play. It uses a tree search for policy improvement, which is subsequently used for training. This approach was able to defeat the previous state of the art methods. Generating training data of high quality requires a lot of computationally expensive iterations, which makes them algorithm slow to train. Experiments show that the strength of the play is growing with each subsequent iteration, this might indicate that it still has room for improvement with more training and that it has not reached its full potential.
Úplný záznam

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