Abstract
In order to realize the mapping from spatial information to temporal information, Temporal Smoothing (TS) Learning is proposed. In this learning, the output of a learning unit, to which sensory signals are given as input, is trained to be smooth along time. In other words, the learning unit is trained so as that the second time derivative of the output itself becomes 0.
This learning can be applied to integrate local sensory signals into an analog spatial signal. It also can be used that an agent learn evaluation function in delayed reinforcement learning on behalf of TD Learning(8)(7) when only one target state is chosen. When a neural network was employed as a learning unit and visual signals were given as inputs directly, the hidden neurons in the neural network represented spatial information and had a adaptability of changing the representation according to the agent's motion characteristics.
