# Free-Lunch Saliency via Attention in Atari Agents

## TL;DR

This paper introduces FLS, an attention-based method for visualizing saliency in Atari reinforcement learning agents, which maintains performance while providing interpretable input importance maps.

## Contribution

The paper presents a novel attention module, FLS, that enables saliency visualization in deep RL agents without sacrificing their performance.

## Key findings

- FLS module produces accurate saliency maps for Atari agents.
- FLS does not degrade the agents' performance compared to baseline.
- Saliency metrics show the method's effectiveness on human gameplay data.

## Abstract

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.