# Policy Disruption in Reinforcement Learning:Adversarial Attack with Large Language Models and Critical State Identification

arXiv: https://arxiv.org/abs/2507.18113 · HTML: https://mlsanity.com/paper/2507.18113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [Adversarial Policies: Attacking Deep Reinforcement Learning](https://mlsanity.com/paper/1905.10615/) (2019)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [Adaptive Reward-Poisoning Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12613/) (2020)
- [Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12909/) (2020)
- [Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning](https://mlsanity.com/paper/2001.09684/) (2020)
- [Robustifying Reinforcement Learning Agents via Action Space Adversarial Training](https://mlsanity.com/paper/2007.07176/) (2020)
- [Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving](https://mlsanity.com/paper/2208.12263/) (2022)
- [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)
- [Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning](https://mlsanity.com/paper/2106.15860/) (2021)
- [RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models](https://mlsanity.com/paper/2403.06420/) (2024)
- [Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2307.07670/) (2023)
- [Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses](https://mlsanity.com/paper/2106.08746/) (2021)
- [Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL](https://mlsanity.com/paper/2305.17342/) (2023)

## Abstract (arXiv)

Reinforcement learning (RL) has achieved remarkable success in fields like robotics and autonomous driving, but adversarial attacks designed to mislead RL systems remain challenging. Existing approaches often rely on modifying the environment or policy, limiting their practicality. This paper proposes an adversarial attack method in which existing agents in the environment guide the target policy to output suboptimal actions without altering the environment. We propose a reward iteration optimization framework that leverages large language models (LLMs) to generate adversarial rewards explicitly tailored to the vulnerabilities of the target agent, thereby enhancing the effectiveness of inducing the target agent toward suboptimal decision-making. Additionally, a critical state identification algorithm is designed to pinpoint the target agent's most vulnerable states, where suboptimal behavior from the victim leads to significant degradation in overall performance. Experimental results in diverse environments demonstrate the superiority of our method over existing approaches.