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

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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.