Exploration of a Breakthrough Technology for Emergence of Symbol Processing by Neuro-based Reinforcement Learning Considering Time Axis

在海量的时空信息中进行有效的学习是在现实世界中产生高级功能的关键。在本研究中，重点研究了时间轴的处理。提出了一种名为“因果关系痕迹”的新思想，它可以“主观地”判断事件的重要性，并用于回顾性学习。该方法在价值学习方面的学习性能优于传统方法。其次，基于“概念”是由必要运动的差异形成的思想，确认了离散和抽象的内部状态表征是通过强化学习在递归神经网络中自主形成的。此外，在自主交际学习中，目标运动的信息可以在学习后传递。时间轴的处理可以引入一个新的视角，但对于符号处理的出现，动力学的学习还需要进一步完善。

Efficient learning in a huge amount of spatio-temporal information holds the key to the emergence of higher functions in the real world. In this research, handling of time axis was especially focused on. A novel idea named "Causality traces" is propounded which judge the importance of events "subjectively" and are used for retrospective learning. Its learning performance exceeds that with the conventional method in value learning. Next, based on the idea that "concept" is formed from the difference of necessary motions, it is confirmed that discrete and abstract internal state representations are autonomously formed in a recurrent neural network through reinforcement learning. Furthermore, in autonomous communication learning, it was shown that information about target movement could be transmitted after learning. A novel perspective could be introduced to the handling of the time axis, but for the emergence of symbol processing, learning of dynamics should be further improved.