We propose a new approach to visualize saliency maps for deep neural network models and apply it to deep reinforcement learning agents trained on Atari environments. Our method adds an attention module that we call FLS (Free Lunch Saliency) to the feature extractor from an established baseline (Mnih et al., 2015). This addition results in a trainable model that can produce saliency maps, i.e., visualizations of the importance of different parts of the input for the agent's current decision making. We show experimentally that a network with an FLS module exhibits performance similar to the baseline (i.e., it is "free", with no performance cost) and can be used as a drop-in replacement for reinforcement learning agents. We also design another feature extractor that scores slightly lower but provides higher-fidelity visualizations. In addition to attained scores, we report saliency metrics evaluated on the Atari-HEAD dataset of human gameplay.

本文提出了一种新方法，通过添加自由午餐显著性（FLS）的注意力模块将其应用于深度学习代理在Atari环境中进行的训练，使网络产生注重分布。通过实验，我们发现网络的性能与基线类似，并且可以作为深度强化学习代理的可替代品。

基于注意力机制的 Atari 智能体自由午餐显著性