摘要：It is propounded that in order to avoid the “frame problem” or “symbol grounding problem” and to create a way to analyze and realize human-like intelligence with higher functions, it is not enough just to introduce deep learning, but it is significant to get out of deeply penetrated “division into functional modules” and to take the approach of “function emergence through end-to-end reinforcement learning.” The functions that have been shown to emerge according to this approach in past works are summarized, and the reason for the difficulty in the emergence of thinking that is a typical higher function is made clear. It is claimed that the proposed hypothesis that exploration grows towards think- ing through learning, becomes a key to break through the difficulty. To realize that, “reinforcement learning using a chaotic neural network” in which adding external ex- ploration noises is not necessary is introduced. It is shown that a robot with two wheels and a simple visual sensor can learn an obstacle avoidance task by using this new reinforcement learning method.
关键词：deep learning ; end-to-end reinforcement learning ; function emergence ; {recurrent or chaotic} neural network ; exploration ; thinking
