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Adversarial Reinforcement Learning Papers

This is a collection of adversarial reinforcement learning papers. Each category is a potential start point for you to start your research. Some papers are listed more than once because they belong to multiple categories.

Adversarial reinforcement learning is closely related to robust reinforcement learning and attacks in reinforcement learning. If you are looking for papers in adversarial reinforcement learning, you should also see papers related to robust reinforcement learning and attacks in reinforcement learning.

For MARL resources, please refer to Multi Agent Reinforcement Learning papers, MARL Papers with Code and MARL Resources Collection.

I will continually update this repository and I welcome suggestions. (missing important papers, missing categories, invalid links, etc.) This is only a first draft so far and I'll add more resources in the next few months.

Update (2026): Added a batch of recent adversarial / robust RL papers (2022–2026) to both the Single-Agent and Multi-Agent sections.

This repository is not for commercial purposes.

My email: chenhao915@mails.ucas.ac.cn

Overview

Single-Agent

Paper Code Accepted at Year
Robust Adversarial Reinforcement Learning Non-official implements on GitHub ICML 2017
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations https://github.com/chenhongge/StateAdvDRL NIPS 2020
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training 2022
Risk Averse Robust Adversarial Reinforcement Learning ICRA 2019
Robust Deep Reinforcement Learning with Adversarial Attacks 2017
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary https://github.com/huanzhang12/ATLA_robust_RL ICLR 2021
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations 2021
RoMFAC: A Robust Mean-Field Actor-Critic Reinforcement Learning against Adversarial Perturbations on States 2022
Adversary Agnostic Robust Deep Reinforcement Learning TNNLS 2021
Learning to Cope with Adversarial Attacks 2019
Adversarial Attack on Graph Structured Data ICML 2018
Characterizing Attacks on Deep Reinforcement Learning AAMAS 2022
Adversarial policies: Attacking deep reinforcement learning https://github.com/HumanCompatibleAI/adversarial-policies ICLR 2020
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization AAAI 2022
On the Robustness of Safe Reinforcement Learning under Observational Perturbations 2022
Robust Reinforcement Learning using Adversarial Populations 2020
Robust Deep Reinforcement Learning through Adversarial Loss https://github.com/tuomaso/radial_rl_v2 NIPS 2021
WocaR-RL: Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning https://github.com/umd-huang-lab/WocaR-RL NeurIPS 2022
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation NeurIPS 2023
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model NeurIPS 2023
GRAD: Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations ICLR 2024
PROTECTED: Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies https://github.com/umd-huang-lab/PROTECTED ICLR 2024
SortRL: Improve Robustness of RL against Observation Perturbations via l∞ Lipschitz Policy Networks AAAI 2024
ReCePS: Reward Certification for Policy Smoothed Reinforcement Learning https://github.com/TrustAI/ReCePS AAAI 2024
QARL: Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula ICLR 2024
DMBP: Diffusion Model-Based Predictor for Robust Offline RL against State Observation Perturbations https://github.com/zhyang2226/DMBP ICLR 2024
RIQL: Towards Robust Offline Reinforcement Learning under Diverse Data Corruption https://github.com/YangRui2015/RIQL ICLR 2024
Distributionally Robust RL with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm NeurIPS 2024
C-ACoE: On Minimizing Adversarial Counterfactual Error in Adversarial Reinforcement Learning ICLR 2025

Multi-Agent

Paper Code Accepted at Year
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems 2022
Distributed Multi-Agent Deep Reinforcement Learning for Robust Coordination against Noise 2022
On the Robustness of Cooperative Multi-Agent Reinforcement Learning IEEE Security and Privacy Workshops 2020
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning CVPR workshop 2022
Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient AAAI 2019
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations NIPS Deep Reinforcement Learning Workshop 2018
Policy Regularization via Noisy Advantage Values for Cooperative Multi-agent Actor-Critic methods 2021
AMI: Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence https://github.com/DIG-Beihang/AMI 2023
ROMANCE: Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers AAAI 2023
ERNIE: Robust Multi-Agent Reinforcement Learning via Adversarial Regularization NeurIPS 2023
RTCA: Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents ECAI 2023
SAMG: What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? TMLR 2024
ADMAC: Robust Communicative Multi-Agent Reinforcement Learning with Active Defense AAAI 2024
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems CCS 2024
MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization 2024
WALL: Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning https://github.com/sunwoolee0504/WALL ICML 2025

Adversarial Communication

Paper Code Accepted at Year
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems 2022
MA3C: Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation Frontiers of Computer Science 2024
ADMAC: Robust Communicative Multi-Agent Reinforcement Learning with Active Defense AAAI 2024
DMAC: Robust Multi-agent Communication Based on Decentralization-Oriented Adversarial Training 2025

Benchmark

Paper Code Accepted at Year
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning CVPR workshop 2022

Citation

If you find this repository useful, please cite our repo:

@misc{chen2022adversarial,
  author={Chen, Hao},
  title={Adversarial Reinforcement Learning Papers},
  year={2022}
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/TimeBreaker/Adversarial-Reinforcement-Learning-Papers}}
}