![]() ![]() Computing Elo ratings of move patterns in the game of Go. In 15th European Conference on Machine Learning, 282–293 (2006)Ĭoulom, R. In 5th International Conference on Computers and Games, 72–83 (2006) Efficient selectivity and backup operators in Monte-Carlo tree search. In 10th International Conference on Advances in Computer Games, 159–174 (2003)Ĭoulom, R. In Advances in Neural Information Processing, 1068–1074 (1996) On-line policy improvement using Monte-Carlo search. In 1st International Conference on Computers and Games, 126–145 (1999) From simple features to sophisticated evaluation functions. A world championship caliber checkers program. Limburg, Maastricht, The Netherlands (1994) Searching for Solutions in Games and Artificial Intelligence. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.Īllis, L. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. ![]() The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. ![]()
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