arXiv:2606.29867v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade performance. Research in adversarial reinforcement learning is often limited by fragmented implementations, inconsistent evaluation protocols, and poor reproducibility. To address these challenges, we present \textbf{RoAd-RL}, an open-source benchmarking framework that provides unified abstractions for policies, attacks, defenses, and robustness metrics, together with
The increasing deployment of DRL in critical applications necessitates robust systems, and the current fragmented research landscape requires standardized solutions.
This framework offers a unified approach to evaluate and improve the security and reliability of AI systems, particularly in autonomous decision-making scenarios.
The ability to systematically benchmark and develop robust AI systems will accelerate their safe integration into real-world applications.
- · AI safety researchers
- · Robotics and autonomous systems developers
- · Defense technology sector
- · Adversarial attackers
- · Systems with unaddressed vulnerabilities
Improved resilience of AI models against malicious attacks and unexpected perturbations.
Faster adoption of AI in high-stakes environments due to increased trust and reliability.
Reduced risk of AI-driven failures in critical infrastructure, potentially impacting national security and economic stability.
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