# Robust Deep Reinforcement Learning with Adversarial Attacks

arXiv: https://arxiv.org/abs/1712.03632 · HTML: https://mlsanity.com/paper/1712.03632/

## Key facts

- Cited by 154 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 30 of 154)

- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
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- [Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs](https://mlsanity.com/paper/2110.05038/) (2021)
- [RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2204.12581/) (2022)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Online Robust Reinforcement Learning with Model Uncertainty](https://mlsanity.com/paper/2109.14523/) (2021)
- [RORL: Robust Offline Reinforcement Learning via Conservative Smoothing](https://mlsanity.com/paper/2206.02829/) (2022)
- [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)
- [Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage](https://mlsanity.com/paper/2305.09659/) (2023)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (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)
- [Policy Smoothing for Provably Robust Reinforcement Learning](https://mlsanity.com/paper/2106.11420/) (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)
- [Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/2209.06620/) (2022)
- [COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks](https://mlsanity.com/paper/2203.08398/) (2022)
- [Evaluating the Robustness of Collaborative Agents](https://mlsanity.com/paper/2101.05507/) (2021)
- [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)
- [Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness](https://mlsanity.com/paper/2301.07487/) (2023)
- [Center Smoothing: Certified Robustness for Networks with Structured Outputs](https://mlsanity.com/paper/2102.09701/) (2021)
- [Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning](https://mlsanity.com/paper/2403.09621/) (2024)
- [Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies](https://mlsanity.com/paper/2405.19424/) (2024)
- [Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs](https://mlsanity.com/paper/2110.08984/) (2021)
- [RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors](https://mlsanity.com/paper/2412.10713/) (2024)
- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)

## Abstract (arXiv)

This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss function which leads to further degradation in performance. These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Cart-pole, Mountain Car, Hopper and Half Cheetah environment.