Abstract
Reinforcement learning has important aspects for exploratory try-and-error and delayed reward. However, it needs many learning cycles and takes a long time to get a sufficient reliable learned result. In order to solve the problem, there is a transfer learning, which requires how to decide the transferring knowledge and how to transfer it. Additionally, an achieved knowledge is not always suited for the task in the real world. In this research, we proposed a communication method based on PCA and discussed these problems with several methods and simple simulation.
