Survey on Adversarial Training Methods for Robust Deep Reinforcement Learning
Researchers have published a comprehensive 83-page survey on adversarial attacks and adversarial training techniques aimed at improving the robustness of deep reinforcement learning (DRL) systems. DRL agents, while powerful in controlled environments, remain vulnerable to small perturbations in observations or environmental dynamics that can degrade performance. The survey matters because it systematically categorizes existing methods and compares their objectives, offering a structured reference for researchers working to make autonomous DRL agents more reliable in real-world deployments.
A survey paper posted to arXiv (cs.LG) provides an in-depth analysis of adversarial attack and adversarial training methodologies applied to deep reinforcement learning, spanning 83 pages, 17 figures, 3 tables, and 15 algorithms. Deep reinforcement learning trains autonomous agents to take sequential actions in complex environments, but the field has long grappled with brittleness: even minor, unexpected changes in conditions can cause significant performance drops. The authors argue that adversarial training — exposing agents to carefully constructed attacks on their observations and on environment dynamics during training — is a promising path toward greater robustness. The survey systematically categorizes contemporary methods, comparing their goals and operational mechanisms to clarify how different approaches relate to one another. The paper has gone through three revisions since its initial submission in March 2024, with the most recent update in June 2026, suggesting ongoing refinement as the field evolves. By consolidating this landscape, the work aims to support both researchers developing new defenses and practitioners seeking to deploy DRL agents in safety-critical or unpredictable real-world settings.
What different sources said
- arXiv cs.LGCenter
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
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