# Online Robustness Training for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1911.00887 · HTML: https://mlsanity.com/paper/1911.00887/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2019.

## Built on by (most cited first, 26 of 26)

- [Robust Reinforcement Learning on State Observations with Learned Optimal Adversary](https://mlsanity.com/paper/2101.08452/) (2021)
- [Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL](https://mlsanity.com/paper/2106.05087/) (2021)
- [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)
- [COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks](https://mlsanity.com/paper/2203.08398/) (2022)
- [Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation](https://mlsanity.com/paper/2308.10306/) (2023)
- [RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors](https://mlsanity.com/paper/2412.10713/) (2024)
- [Admissible Policy Teaching through Reward Design](https://mlsanity.com/paper/2201.02185/) (2022)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective](https://mlsanity.com/paper/2501.03562/) (2025)
- [Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems](https://mlsanity.com/paper/2206.10158/) (2022)
- [Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL](https://mlsanity.com/paper/2305.17342/) (2023)
- [Improve Robustness of Reinforcement Learning against Observation Perturbations via l_∞ Lipschitz Policy Networks](https://mlsanity.com/paper/2312.08751/) (2023)
- [Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error](https://mlsanity.com/paper/2402.02165/) (2024)
- [Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network](https://mlsanity.com/paper/2104.03154/) (2021)
- [Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation](https://mlsanity.com/paper/2301.11374/) (2023)
- [Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach](https://mlsanity.com/paper/2507.17070/) (2025)
- [Security of Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2212.06123/) (2022)
- [Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimization](https://mlsanity.com/paper/2507.03372/) (2025)
- [TCRL: Temporal-Coupled Adversarial Training for Robust Constrained Reinforcement Learning in Worst-Case Scenarios](https://mlsanity.com/paper/2602.13040/) (2026)
- [Measuring Interventional Robustness in Reinforcement Learning](https://mlsanity.com/paper/2209.09058/) (2022)
- [Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations](https://mlsanity.com/paper/2307.12062/) (2023)
- [On the Perturbed States for Transformed Input-robust Reinforcement Learning](https://mlsanity.com/paper/2408.00023/) (2024)
- [Towards Robust Deep Reinforcement Learning against Environmental State Perturbation](https://mlsanity.com/paper/2506.08961/) (2025)
- [A Dynamical Systems Framework for Reinforcement Learning Safety and Robustness Verification](https://mlsanity.com/paper/2508.15588/) (2025)
- [On the Tension Between Optimality and Adversarial Robustness in Policy Optimization](https://mlsanity.com/paper/2512.01228/) (2025)

## Abstract (arXiv)

In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training. We propose a system called Robust Student-DQN (RS-DQN), which permits online robustness training alongside Q networks, while preserving competitive performance. We show that RS-DQN can be combined with (i) state-of-the-art adversarial training and (ii) provably robust training to obtain an agent that is resilient to strong attacks during training and evaluation.