Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey

📰 ArXiv cs.AI

Learn how to improve the robustness of Deep Reinforcement Learning (DRL) through adversarial attacks and training, and why it matters for real-world applications

advanced Published 10 Jun 2026

Action Steps

Apply adversarial attacks to DRL models to test their robustness
Train DRL models using adversarial training methods to improve their reliability
Configure DRL models to handle minor condition variations
Test DRL models in complex environments to evaluate their performance
Build robust DRL models using techniques such as input validation and data augmentation

Who Needs to Know This

AI engineers and researchers on a team benefit from this knowledge to develop more reliable and trustworthy DRL models, which is crucial for deploying them in real-world environments

Key Insight

💡 Adversarial attacks and training can significantly improve the robustness of DRL models, making them more reliable and trustworthy in real-world applications

Key Takeaways

Learn how to improve the robustness of Deep Reinforcement Learning (DRL) through adversarial attacks and training, and why it matters for real-world applications

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