Free-Lunch Saliency via Attention in Atari Agents
This provides a method for interpretability in reinforcement learning agents, but it is incremental as it builds on existing baselines.
The paper tackles the problem of visualizing saliency maps for deep reinforcement learning agents in Atari environments by adding a trainable attention module (FLS) to a baseline feature extractor, resulting in no performance cost and enabling drop-in replacement with similar scores and improved visualizations.
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.