![]() ![]() © 2017 Macmillan Publishers Limited, part of Springer Nature. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. ![]() These players are on the right track, with continuous training, they would become better Gomoku players.Ī long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Therefore, this thesis proves that it is possible for the machine Gomoku player to evolve by itself without human knowledge. Although even the latest players do not have strong capacities and thus couldn’t be regarded as strong AI agents, they still show the abilities to learn from the previous games. During the training, beginning with zero knowledge, these players developed a row-based bottom-up strategy, followed by a column-based bottom-up strategy, and finally, a more flexible and intelligible strategy with a preference to the surrounding squares. The later these players were generated, the stronger abilities they have. We have run this system for a month and half, during which time 150 different players were generated. They develop their own skills from scratch by themselves. These are self-evolving players that no prior knowledge is given. Inspired by Google’s AlphaGo Zero, in this thesis, by combining the technologies of Monte Carlo Tree Search, Deep Neural Networks, and Reinforcement Learning, we propose a system that trains machine Gomoku players without prior human skills. However, the computer just plays following the pre-entered skills, it doesn’t know how to develop these skills by itself. Scientists normalize and enter these skills into the computer so that the computer knows how to play Gomoku. We humans, as players, also created a lot of skills in playing it. For a long time, it has brought countless pleasures to us. ![]() Gomoku, also called Five in a row, is one of the earliest checkerboard games invented by humans. ![]()
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