Deep learning has been widely applied to various fields in a recent decade, however most of them aim at solving specific problems. Deep reinforcement learning （DRL） is a combination of deep learning and reinforcement learning, which uses neural networks to learn from a predetermined reward function based on the environmental feedback, thus it is capable of solving multiple problems at the same time. DeepMind provides a method called Rainbow DQN[1], which combines six modifications in the field of DRL, and it performs better than human masters in some Atari games in 2017. In this study, we introduce a visualization method of the neural network learning together with testing procedure based on Rainbow DQN, by employing an encoder-decoder architecture that uses a part of the original neural network as an encoder, some additional layers as embedding method, and an extra reversed neural network as the decoder. We further conduct the analysis of the gaming behavior, and observe better gaming performance in some Atari games.