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. 2019 Jun 28;13:40. doi: 10.3389/fnbot.2019.00040

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Copyright © 2019 Chen, Chen, Zhang and Hu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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We visualized the first layer and obtain a direct view (left). However, many grids contain unreadable information such as the grid marked with a green frame. Because the first layer's output was quite abstract, we then visualized the last layer using deconvolution and obtained the right picture. It seemed to represent the wall element in the original graph. That means the agent could pay attention to the wall and then take suitable actions. Panel (A) shows the direct view visualization of the first layer of CNN. Panel (B) shows the visualization of the last layer of CNN using deconvolution.