arXiv:2606.00270v1 Announce Type: cross Abstract: Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs). However, existing shielding techniques typically assume knowledge of the safety-relevant transition dynamics - a requirement that is seldom met in practice. To address this limitation, we introduce a novel shielding framework for robust MDPs (RMDPs), i.e., MDPs with sets of transition probabilities. We define safety as the satisfaction of a linear temporal logic (LTL) formula with a certain threshold probabil
The increasing deployment of AI in safety-critical applications necessitates robust mechanisms to guarantee their safe operation, especially in environments with uncertain dynamics.
This development addresses a fundamental limitation in current AI safety techniques, moving towards more reliable and deployable autonomous systems in real-world, uncertain conditions.
Existing shielding methods, typically reliant on perfect knowledge of system dynamics, can now be applied in scenarios where only probabilistic boundaries or sets of transition probabilities are known.
- · AI developers
- · Autonomous systems manufacturers
- · Safety-critical industries (e.g., automotive, aerospace)
- · Opponents of AI deployment
- · Purely reactive AI systems without safety guarantees
Increased trustworthiness and deployment acceleration of reinforcement learning agents in complex, real-world scenarios.
Reduced regulatory hurdles and insurance costs for AI-powered systems due to demonstrable safety guarantees.
Broader societal acceptance and integration of autonomous AI into daily life as perceived risks diminish.
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