Autonomous, Harmonious and Purposive Acquisition of Various Functions of Robots by Reinforcement Learning and the Relation to the Intelligence Formation

这项研究的目的是表明，通过使用强化学习得到的训练信号进行学习，在传感器信号直接输入的神经网络中根据需要产生各种函数，其输出是运动指令。主要研究成果如下：1。有人说，神经网络不擅长符号处理。然而，研究表明，只有通过强化学习，神经网络的输出表示才会变成二进制。研究表明，真实的机器人可以利用神经网络学习推箱行为，而不需要给出任何关于图像处理、图像识别或给定任务的信息。研究表明，即使在存在各种物体和五颜六色的传单的准现实世界中，真实的机器人也可以在一定程度上学习到达对象。结果表明，通过强化学习训练的递归神经网络可以学习一些被认为与空间或时间抽象有关的任务。

This research was aimed to show that by the learning using the training signals that are derived by reinforcement learning, various functions emerge according to the necessity in a neural network to which sensor signals are directly entered and whose outputs are motor commands. The main fruits are as follows.1. It is said that neural networks are not good at symbol processing. However, it was shown that the output representation of a neural network became binary only by reinforcement learning.2. It was shown that a real robot could learn box-pushing behavior using neural network without giving any informatio a about image processing, image recognition, or the given task.3. It was shown that a real robot could learn to reach an object in some degree even in a quasi-real world where various objects and colorful leaflets exist.4. It was shown that a recurrent neural network trained by reinforcement learning could learn some tasks that are thought to be relevant to the spatial or temporal abstraction.