arXiv:2403.00420v3 Announce Type: replace Abstract: Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant performance in well-known environments, it remains susceptible to minor condition variations, raising concerns about its reliability in real-world applications. To improve usability, DRL must demonstrate trustworthiness and robustness. A way to improve the robustness of DRL to unknown changes in the environmental conditions and possible perturbations is through Adv
The increasing deployment of DRL in real-world critical applications, coupled with growing concerns over AI safety and reliability, makes the robustness of these systems a critical area of research right now.
This survey highlights how adversarial machine learning techniques are essential for developing trustworthy AI, which is crucial for wider adoption and impact of autonomous systems in sensitive environments.
The focus on adversarial attacks and training provides a clear pathway for making DRL more resilient to unexpected conditions, thus expanding the scope of its reliable application beyond controlled environments.
- · AI developers
- · Robotics companies
- · Defence sector
- · Critical infrastructure operators
- · Developers of fragile DRL systems
Increased reliability of AI systems in real-world, dynamic environments.
Faster adoption and regulatory approval for autonomous systems in high-stakes applications.
Shift in AI development best practices to inherently include robustness and adversarial testing from early stages.
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Read at arXiv cs.LG