arXiv:2606.03521v1 Announce Type: new Abstract: To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations. In this setting, a protagonist agent optimizes a policy under environmental perturbations from an adversary, resulting in a zero-sum Markov game. When adversarially robust RL is combined with model-based RL, the adversary can target a learned transition model instead of the training environment. Extending this idea, this work introduces post-hoc robustification of dee
The increasing deployment of AI in critical real-world applications drives the necessity for robust and reliable models, making vulnerability to adversarial attacks a pressing concern.
Improving adversarial robustness in AI agents is crucial for their safe and effective operation in complex, unpredictable environments, a key challenge for widespread AI adoption.
The focus extends beyond training robust agents to developing post-hoc methods for securing existing model-based reinforcement learning systems against adversarial perturbations.
- · AI developers
- · Robotics companies
- · Defense technology sector
- · Critical infrastructure operators
- · Adversaries targeting AI systems
- · Unsecured AI models
Increased trust and reliability in deployed AI and autonomous systems.
Faster adoption of AI in sensitive applications where robustness is paramount.
The development of a new niche in AI security specializing in post-hoc robustness for learned models.
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Read at arXiv cs.LG