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

本研究旨在表明，通过使用强化学习衍生的训练信号进行学习，在直接输入传感器信号并输出运动命令的神经网络中，根据需要出现各种功能。主要的水果如下。据说神经网络不擅长符号处理。然而，只有通过强化学习，神经网络的输出表示才会变成二值。结果表明，机器人可以在不提供任何图像处理、图像识别或给定任务信息的情况下，利用神经网络学习推盒行为。结果表明，即使在存在各种物体和彩色传单的准真实世界中，真正的机器人也能在一定程度上学会够到物体。研究表明，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相关的任务。

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.