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

A comprehensive survey examines adversarial attacks and training methodologies for improving Deep Reinforcement Learning robustness. The research addresses DRL's vulnerability to environmental perturbations and condition variations, proposing adversarial training as a key mechanism to enhance agent reliability in real-world deployments.

Deep Reinforcement Learning has demonstrated impressive performance in controlled environments, yet faces critical limitations when deployed in real-world scenarios where conditions inevitably deviate from training parameters. This survey tackles a fundamental challenge in autonomous systems: the gap between laboratory performance and production reliability. The research systematically categorizes adversarial attack and training methodologies, providing a structured framework for understanding how to fortify DRL agents against unexpected perturbations.

The vulnerability of DRL systems stems from their training on static, well-defined environments where agents learn brittle policies sensitive to minor variations. This brittleness becomes problematic in applications ranging from autonomous vehicles to robotics, where environmental unpredictability is the norm. Adversarial training—deliberately exposing agents to adversarially-crafted scenarios during training—mirrors established robustness techniques in computer vision and NLP, but applied to the sequential decision-making domain.

For the AI and autonomous systems industry, this work advances the practical deployment timeline of DRL in safety-critical applications. Organizations developing autonomous agents can reference comprehensive adversarial methodologies to pre-emptively strengthen their systems, reducing costly failures in production. The systematic comparison of attack and defense mechanisms enables practitioners to select appropriate strategies for their specific use cases, balancing robustness against computational overhead.

Looking forward, this survey establishes baseline understanding for emerging robust DRL systems. Future work likely focuses on efficiency gains in adversarial training, formal verification of robustness guarantees, and application-specific adversarial strategies. As autonomous systems proliferate across industries, the ability to maintain performance under adversarial conditions becomes a market differentiator.

• →Deep Reinforcement Learning systems are vulnerable to minor environmental variations despite strong performance in controlled settings.
• →Adversarial training against observations and dynamics perturbations significantly improves DRL robustness and real-world reliability.
• →The survey systematically categorizes contemporary attack and defense methodologies, enabling practitioners to select appropriate strategies.
• →Robust DRL systems accelerate deployment of autonomous agents in safety-critical applications across robotics and autonomous vehicles.
• →Adversarial training establishes new industry standards for trustworthiness in machine learning-based autonomous systems.