# Delving into adversarial attacks on deep policies

arXiv: https://arxiv.org/abs/1705.06452 · HTML: https://mlsanity.com/paper/1705.06452/

## Key facts

- Cited by 104 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2017.

## Built on by (most cited first, 30 of 104)

- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Robust Reinforcement Learning on State Observations with Learned Optimal Adversary](https://mlsanity.com/paper/2101.08452/) (2021)
- [Online Robust Reinforcement Learning with Model Uncertainty](https://mlsanity.com/paper/2109.14523/) (2021)
- [Policy Gradient Method For Robust Reinforcement Learning](https://mlsanity.com/paper/2205.07344/) (2022)
- [Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL](https://mlsanity.com/paper/2106.05087/) (2021)
- [Sound Adversarial Audio-Visual Navigation](https://mlsanity.com/paper/2202.10910/) (2022)
- [Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning](https://mlsanity.com/paper/2210.05927/) (2022)
- [CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing](https://mlsanity.com/paper/2106.09292/) (2021)
- [On the Robustness of Safe Reinforcement Learning under Observational Perturbations](https://mlsanity.com/paper/2205.14691/) (2022)
- [Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability](https://mlsanity.com/paper/2209.08025/) (2022)
- [Robust Multi-Agent Reinforcement Learning with State Uncertainty](https://mlsanity.com/paper/2307.16212/) (2023)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning](https://mlsanity.com/paper/2110.04471/) (2021)
- [Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments](https://mlsanity.com/paper/2102.08492/) (2021)
- [COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks](https://mlsanity.com/paper/2203.08398/) (2022)
- [What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?](https://mlsanity.com/paper/2212.02705/) (2022)
- [Robust Average-Reward Markov Decision Processes](https://mlsanity.com/paper/2301.00858/) (2023)
- [Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning](https://mlsanity.com/paper/2208.13663/) (2022)
- [Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness](https://mlsanity.com/paper/2301.07487/) (2023)
- [Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence](https://mlsanity.com/paper/2302.03322/) (2023)
- [Stabilizing Unsupervised Environment Design with a Learned Adversary](https://mlsanity.com/paper/2308.10797/) (2023)
- [Adversarial Cheap Talk](https://mlsanity.com/paper/2211.11030/) (2022)
- [Investigating Vulnerabilities of Deep Neural Policies](https://mlsanity.com/paper/2108.13093/) (2021)
- [Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum](https://mlsanity.com/paper/2206.10057/) (2022)
- [Robust Constrained Reinforcement Learning](https://mlsanity.com/paper/2209.06866/) (2022)
- [Are AlphaZero-like Agents Robust to Adversarial Perturbations?](https://mlsanity.com/paper/2211.03769/) (2022)
- [Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks](https://mlsanity.com/paper/2302.13851/) (2023)
- [Multi-Task Federated Reinforcement Learning with Adversaries](https://mlsanity.com/paper/2103.06473/) (2021)
- [Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning](https://mlsanity.com/paper/2106.15860/) (2021)
- [Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns](https://mlsanity.com/paper/2109.07723/) (2021)

## Abstract (arXiv)

Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polices. We compare the effectiveness of the attacks using adversarial examples vs. random noise. We present a novel method for reducing the number of times adversarial examples need to be injected for a successful attack, based on the value function. We further explore how re-training on random noise and FGSM perturbations affects the resilience against adversarial examples.