Visualization of deep reinforcement learning using Grad-CAM: How AI plays atari games?

Abstract

Deep Reinforcement Learning (DRL) allows agents to learn strategies to solve complex tasks. It has been applied to solve various problems such as natural language processing, games, etc. However, it is still difficult to apply DRL to certain real-world problems because each action is not predictable, and we cannot know why the results are coming out. For this reason, a technology called eXplainable Artificial Intelligence (XAI) has been recently developed. As this technology shows a visualization of the AI process, people can easily understand the results of AI. In this paper, we proposed to use Grad-CAM, one of the XAI techniques, when we visualize the behaviors of AI players trained by DRL. Our experimental results show which part of the input state is focused on when one well-trained agent takes action.