# Robust Deep Reinforcement Learning through Adversarial Loss

arXiv: https://arxiv.org/abs/2008.01976 · HTML: https://mlsanity.com/paper/2008.01976/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 30 of 75)

- [RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2204.12581/) (2022)
- [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)
- [Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability](https://mlsanity.com/paper/2209.08025/) (2022)
- [COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks](https://mlsanity.com/paper/2203.08398/) (2022)
- [Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness](https://mlsanity.com/paper/2301.07487/) (2023)
- [RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states](https://mlsanity.com/paper/2205.07229/) (2022)
- [RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors](https://mlsanity.com/paper/2412.10713/) (2024)
- [Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum](https://mlsanity.com/paper/2206.10057/) (2022)
- [Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers](https://mlsanity.com/paper/2305.05909/) (2023)
- [Learning a subspace of policies for online adaptation in Reinforcement Learning](https://mlsanity.com/paper/2110.05169/) (2021)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning](https://mlsanity.com/paper/2106.15860/) (2021)
- [Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks](https://mlsanity.com/paper/2207.10170/) (2022)
- [Distributionally Adaptive Meta Reinforcement Learning](https://mlsanity.com/paper/2210.03104/) (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)
- [Adjustable Robust Reinforcement Learning for Online 3D Bin Packing](https://mlsanity.com/paper/2310.04323/) (2023)
- [Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula](https://mlsanity.com/paper/2311.01642/) (2023)
- [Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective](https://mlsanity.com/paper/2501.03562/) (2025)
- [Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses](https://mlsanity.com/paper/2106.08746/) (2021)
- [Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies](https://mlsanity.com/paper/2402.12673/) (2024)
- [State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning](https://mlsanity.com/paper/2503.20613/) (2025)
- [Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems](https://mlsanity.com/paper/2206.10158/) (2022)
- [Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization](https://mlsanity.com/paper/2207.02016/) (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)
- [Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks](https://mlsanity.com/paper/2503.20844/) (2025)
- [Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation](https://mlsanity.com/paper/2305.05116/) (2023)
- [Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization](https://mlsanity.com/paper/2410.03376/) (2024)

## Abstract (arXiv)

Recent studies have shown that deep reinforcement learning agents are vulnerable to small adversarial perturbations on the agent's inputs, which raises concerns about deploying such agents in the real world. To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against lp-norm bounded adversarial attacks. Our framework is compatible with popular deep reinforcement learning algorithms and we demonstrate its performance with deep Q-learning, A3C and PPO. We experiment on three deep RL benchmarks (Atari, MuJoCo and ProcGen) to show the effectiveness of our robust training algorithm. Our RADIAL-RL agents consistently outperform prior methods when tested against attacks of varying strength and are more computationally efficient to train. In addition, we propose a new evaluation method called Greedy Worst-Case Reward (GWC) to measure attack agnostic robustness of deep RL agents. We show that GWC can be evaluated efficiently and is a good estimate of the reward under the worst possible sequence of adversarial attacks. All code used for our experiments is available at https://github.com/tuomaso/radialrlv2.