Source-linked AI summary
Visualizing and Understanding Atari Agents
Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern
TL;DR
Deep RL agents can maximize rewards without revealing the strategies behind their decisions, creating a need for human-interpretable explanations. This paper introduces perturbation-based saliency maps for Atari agents and uses them to study attention, overfitting, policy learning, and human understanding. The visualizations provide insight into agent decisions and learning behavior, while the authors note that satisfying explanations require multiple techniques.
Problem
Deep RL agents can perform effectively while their decision-making strategies remain unclear, and existing explanations provide limited insight for vision-based policies.
Method
The paper introduces a perturbation-based saliency technique that visualizes how raw image inputs influence A3C actor and critic decisions.
Results
Saliency visualizations yield insight into Atari agents’ decisions and learning behavior, including strong policies, wrong-reason overfitting, low-quality decisions, and non-expert understanding.
Takeaways & Limitations
Saliency information can help people understand what Atari agents attend to and identify how their policies make decisions.
Takeaways & Limitations
The analysis mainly addresses how an agent’s current state affects its current policy, while recurrent-agent memory receives only preliminary visualization experiments.
Abstract
from arXiv · showhide
While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward explaining deep RL agents through a case study using Atari 2600 environments. In particular, we focus on using saliency maps to understand how an agent learns and executes a policy. We introduce a method for generating useful saliency maps and use it to show 1) what strong agents attend to, 2) whether agents are making decisions for the right or wrong reasons, and 3) how agents evolve during learning. We also test our method on non-expert human subjects and find that it improves their ability to reason about these agents. Overall, our results show that saliency information can provide significant insight into an RL agent's decisions and learning behavior.
1. Introduction
Deep RL agents can perform impressively while remaining difficult to interpret, motivating saliency-based tools for understanding their decisions, strategies, and learning behavior. The paper introduces a perturbation-based method and applies it to Atari agents, including strong, overfit, poorly performing, and developing policies.
- Deep learning systems often remain black boxes despite strong performance, creating a need for interpretation when trust and reliability matter.
- Very little prior work explains deep RL agents, even though they operate in challenging environments with sparse rewards and noisy, high-dimensional inputs.
- Saliency is investigated because existing tools are either difficult for non-experts to interpret or provide limited insight into internal decision-making.
- The paper introduces a perturbation-based technique for generating saliency videos and evaluates it in six Atari 2600 environments.
- The investigations examine strong-agent strategies, policy evolution during training, high rewards earned for wrong reasons, and low-quality decisions.
- The paper also considers recurrent-agent memory, noting that prior frames can matter when decisions depend on quantities such as ball velocity.
2. Related Work
Prior approaches explain RL behavior through abstractions or input sensitivities, but vision-based agents require explanations that connect original pixels to individual decisions and remain interpretable to non-experts. The paper therefore builds on perturbation-based visual explanations while addressing limitations of gradient and external policy analyses.
- Traditional RL explanations assume an exact MDP model and interpretable high-level state features, assumptions that do not hold in the paper’s vision-based domain.
- Execution-trace explanations rely on hand-crafted semantic state features, making them impractical for agents that learn directly from pixels.
- SAMDP analysis provides interpretable insight into high-level temporal policy structure but operates externally to the deep policy and misses perceptual aspects.
- The paper seeks visualizations of how inputs influence individual decisions, emphasizing explanations obtained directly from the policy and interpretable to untrained users.
- Gradient-based methods are efficient and mathematically defined, but their perturbations can lack physical meaning and move inputs away from realistic image manifolds.
- Perturbation-based methods instead measure how model outputs change when input information is altered, motivating localized visual interventions.
- Replacing image regions with constant colors can introduce unwanted color and edge information, motivating masked interpolations designed to add less new information.
3. Visualizing Saliency for Atari Agents
The paper visualizes Atari agents by perturbing localized image regions and measuring how those changes affect policy and value outputs. This produces saliency maps intended to reveal which visual information supports action selection and valuation.
- Agent outputs: A3C agents use a policy distribution π and value estimate V^π over observed image-frame sequences.The shared network estimates both the action distribution and expected future value.
- Perturbation-based saliency: The method locally blurs an image region with a Gaussian-weighted mask to add spatial uncertainty around selected coordinates.The perturbation interpolates between the original image and a Gaussian blur; in Pong, it can diffuse the ball’s pixels.
- Policy saliency: Policy saliency measures how much a localized perturbation changes the policy’s logits relative to the original frame sequence.The metric uses the squared difference between original and perturbed policy logits.
- Interpretability: The perturbation-based score approximates a directional gradient constrained toward a local, visually meaningful perturbation rather than an unconstrained input change.The authors hypothesize that this constraint makes the resulting saliency maps more interpretable than gradient-based alternatives.
- Visualization: In practice, saliency is computed on 5-pixel patches, upsampled to the Atari frame resolution, and displayed using blue for policy and red for value.Patch-based computation reduces computational cost while preserving useful visualizations.
- Value saliency: Value saliency is computed analogously from the squared difference between the original and perturbed value estimates.The resulting measure captures each image region’s importance to the policy’s valuation at time t.
4. Experiments
Experiments use saliency to reveal what Atari agents attend to, how their policies change during training, and why some high- or low-performing agents fail. The analyses also examine whether saliency helps people assess robustness and provide preliminary evidence about recurrent memory.
- Understanding Strong Policies: Strong Pong agents attend mainly to their own paddle before a return and to the ball after a kill shot, exploiting deterministic dynamics.The analysis suggests this precise strategy can yield rewards reliably but may overfit to the particular opponent.
- Understanding Strong Policies: SpaceInvaders agents shift from unfocused bullet spraying toward aiming strategies, while actor and critic saliency reveal target tracking and defensive attention.The actor first highlights a target, then the critic tracks it in anticipation of reward; both also monitor the area above the ship.
- Policies During Learning: Training substantially changes attention: early policies are unfocused, whereas later agents attend to task-relevant structures such as tunneling regions, targets, and the ball.Actor and critic saliency often focus on different regions as learning progresses.
- Detecting Overfit Policies: Saliency distinguishes overfit Atari agents from control agents even when behavior alone cannot separate them, identifying regions associated with unintended reward strategies.In all three games, the method pinpointed known-important regions and detected agents earning high rewards for the wrong reasons.
- Visualizations for Non-experts: Saliency helped non-experts judge robustness and identify the visual cues used by agents, including hint pixels for overfit agents and multiple objects for controls.With saliency, 67.7% identified the overfit agent’s hint pixels, while recognition of the control agent’s ball focus fell to 32.3%.
- Debugging with Saliency Maps: In MsPacman, Frostbite, and Enduro, saliency exposed distractor-focused policies that failed to track ghosts, destination platforms, or other racers.For MsPacman, sparse rewards may have prevented agents from inferring that being caught by a ghost was bad.
- Importance of Memory: Preliminary memory experiments found memory most salient immediately before ball-paddle contact in Pong and Breakout, while its role in SpaceInvaders remained unclear.The authors caution that sensitivity near contact may reflect general perturbation sensitivity rather than memory specifically.
5. Summary
The paper introduces a saliency method to visualize and understand Atari agents. It finds that these visualizations can support non-experts while noting that satisfying explanations require multiple techniques.
- The paper introduces a saliency method for visualizing and understanding Atari agents.
- The visualizations can help non-experts understand what agents are doing.
- Human-interpretable explanations of policies require multiple techniques to capture both how and why they act.