#Deeper game Offline#
#Deeper game professional#

Policy gradient methods for reinforcement learning with function approximation. Simple statistical gradient-following algorithms for connectionist reinforcement learning.

In 32nd International Conference on Machine Learning, 1766–1774 (2015) Training deep convolutional neural networks to play go. 3rd International Conference on Learning Representations (2015)Ĭlark, C. Move evaluation in Go using deep convolutional neural networks. In International Conference on Artificial Neural Networks, 101–110 (2008) Mimicking Go experts with convolutional neural networks. In International Conference of Machine Learning, 873–880 (2006) Bayesian pattern ranking for move prediction in the game of Go. Human-level control through deep reinforcement learning. Face recognition: a convolutional neural-network approach. In Advances in Neural Information Processing Systems, 1097–1105 (2012) ImageNet classification with deep convolutional neural networks. In 17th International Conference on Machine Learning, 273–280 (2007)

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Combining online and offline learning in UCT. Fuego – an open-source framework for board games and Go engine based on Monte-Carlo tree search. In Advances in Computer Games, 24–38 (Springer, 2012) Pachi: State of the art open source Go program. 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.

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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.