Survey on Adversarial Training Methods for Robust Deep Reinforcement Learning
Researchers have published a comprehensive survey analyzing adversarial attack and training methodologies for deep reinforcement learning (DRL) systems. The survey addresses a critical vulnerability: DRL agents perform well in controlled environments but fail under minor condition variations, limiting real-world deployment. Improving DRL robustness through adversarial training is essential for building trustworthy autonomous systems that can operate reliably in unpredictable conditions.
A new 83-page survey paper examines how adversarial training can strengthen deep reinforcement learning systems against environmental perturbations and unexpected changes. The research systematically categorizes contemporary adversarial attack and training methodologies, comparing their objectives and operational mechanisms. While DRL has achieved significant performance in well-known environments, its susceptibility to minor variations in conditions poses a major obstacle to real-world applications. The survey argues that adversarial training—exposing agents to well-designed attacks on both observations and environmental dynamics—is a promising approach to enhance robustness. This work provides an in-depth analysis intended to guide future development of more reliable autonomous agents.
What different sources said
- arXiv cs.LGCenter
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
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