# Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?

arXiv: https://arxiv.org/abs/2012.01281 · HTML: https://mlsanity.com/paper/2012.01281/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning](https://mlsanity.com/paper/2209.09203/) (2022)
- [ViSaRL: Visual Reinforcement Learning Guided by Human Saliency](https://mlsanity.com/paper/2403.10940/) (2024)
- [Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning](https://mlsanity.com/paper/2503.06343/) (2025)
- [RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning](https://mlsanity.com/paper/2303.04475/) (2023)
- [Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability](https://mlsanity.com/paper/2205.08685/) (2022)
- [Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense](https://mlsanity.com/paper/2207.13036/) (2022)
- [Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information](https://mlsanity.com/paper/2211.04987/) (2022)
- [Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's Localization and Saliency](https://mlsanity.com/paper/2404.15564/) (2024)
- [SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning](https://mlsanity.com/paper/2512.05291/) (2025)

## Abstract (arXiv)

Deep Reinforcement Learning (DRL) connects the classic Reinforcement Learning algorithms with Deep Neural Networks. A problem in DRL is that CNNs are black-boxes and it is hard to understand the decision-making process of agents. In order to be able to use RL agents in highly dangerous environments for humans and machines, the developer needs a debugging tool to assure that the agent does what is expected. Currently, rewards are primarily used to interpret how well an agent is learning. However, this can lead to deceptive conclusions if the agent receives more rewards by memorizing a policy and not learning to respond to the environment. In this work, it is shown that this problem can be recognized with the help of gradient visualization techniques. This work brings some of the best-known visualization methods from the field of image classification to the area of Deep Reinforcement Learning. Furthermore, two new visualization techniques have been developed, one of which provides particularly good results. It is being proven to what extent the algorithms can be used in the area of Reinforcement learning. Also, the question arises on how well the DRL algorithms can be visualized across different environments with varying visualization techniques.