English
资源说明：阿法狗的算法描述，Google发表在nature上的论文。
Mastering the game of Go with deep neural networks and tree search
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. Here we introduce a new approach
to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. 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. Without any lookahead search, the neural networks play Go at the level of stateof-the-art
Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a
new search algorithm that combines Monte Carlo simulation with value and policy networks. 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. 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.
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