My journey for studying in AI
The first topic my teacher gave me is how to use pressure sensors under two feet to let robot learning to walk without Gyro. So I kept reading all of the paper which written by my laboratory. After I finished reading, I realized there are two shortcomings in those theories. The first thing is if there are too many states, computation will face the curse of dimensionality. And it will compute very slowly even come out with the wrong result. Second, the robot which uses my lab’s theory won’t generate to other situation. If the robot has learned how to walk on the ground successfully, it won’t walk successfully on the sea saw as well. In order to solve these problems, I search many papers to find the answer to the problem. Finally, I found DeepMind have already solved these problems. They used a big deep network to train the robot to play an old game called Atari and it played well, even better than human in some of the games. Everyone was shocked by the company, even Google. That’s why Google bought this company afterward. They don’t use specific states to train the robot. Instead, they train with raw pixels which generated by the screen. If the size of the screen is 100*100 pixels, there would be ten thousands times three, which contain RGB informations, states which equal to thirty thousands. If we use old version of table method, it would face into the curse of dimensionality. DeepMind used deep network wisely to solve this problem.