According to foreign media reports, the research on artificial intelligence of the UK's DeepMind team has made new progress: they have developed a new generation of Go AI-ALphaGo Zero. ALphaGo Zero, which uses intensive learning technology, has a tremendous increase in chess and can easily defeat ALphaGo, which has defeated Ke Jie and Li Shishi.
After defeating Ke Jie, ALphaGo can be said that in the world of Go, it is already the realm of "unique and defeated". Almost no human being is its opponent. But this does not mean that ALphaGo has reached a peak in the field of Go. Therefore, ALphaGo wants to go up to the upper limit of Go knowledge, obviously only it can become its own teacher.
In the past, AlphaGo used the game data of amateur and professional human players to train. Although the use of human chess player data allows ALphaGo to learn human Go skills, human expert data is often difficult to obtain and expensive, plus humans are not machines, and inevitably there will be errors, and the data generated by mistakes may reduce ALphaGo. Chess power. Therefore, ALphaGo Zero adopts intensive learning technology, starting from the immediate game, without relying on any human expert's game data or manual supervision, but letting it improve the game by self-game.
So what exactly is reinforcement learning technology? Simply put, reinforcement learning is about letting AI learn the strategy of getting the most out of it. AlphaGo Zero's reinforcement learning mainly consists of two parts, the Monte Carlo tree search algorithm and the neural network algorithm. In these two algorithms, the neural network algorithm can give the drop scheme according to the current chess situation, and predict which side has a larger win in the current situation; the Monte Carlo search algorithm can be regarded as a current step. Evaluation and improvement tool that simulates where AlphaGo Zero can get a higher winning percentage. If the algorithm of AlphaGoZero's neural network algorithm is closer to the output of the Monte Carlo tree search algorithm, the higher the winning rate, the higher the return. Therefore, for each drop, AlphaGo Zero optimizes the parameters in the neural network algorithm to make the calculated drop scheme closer to the Monte Carlo search algorithm, while minimizing the deviation of the winner prediction.
AlphaGo Zero's self-reinforcing learning, image from Nature
At the beginning, AlphaGoZero's neural network did not understand Go, and only blindly fell. However, after experiencing numerous battles of "left and right", AlphaGo Zero finally grew from a chess rookie to a chess god.
The DeepMind team said that they found that AlphaGo Zero self-played for only a few dozen days and mastered the Go technology that humans have studied over the centuries. Since the entire game process does not use human data, ALphaGo Zero has a unique chess path and is no longer limited to the existing Go theory of human beings.
The DeepMind team also said that the project is not just to gain a deeper understanding of Go, AlphaGoZero shows people that artificial intelligence can make progress even without human data. Ultimately these technological advances should be used to solve real-world problems such as protein folding or new material design. This will enhance human cognition and improve everyone's life.
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