arXiv:2602.03778v2 Announce Type: replace Abstract: Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastrophic events. Unlike risk-neutral objectives, the static CVaR of the return depends on entire trajectories without admitting a recursive Bellman decomposition in the underlying Markov decision process. A classical resolution relies on state augmentation with a continuous variable. However, unless restricted to a specialized class of admissible value functions, this formulation induces sparse rewards and d
The paper addresses a long-standing challenge in applying risk-sensitive control to real-world safety-critical AI systems, building on recent advances in reinforcement learning theory.
This research provides a theoretical advancement in ensuring AI systems, particularly in safety-critical applications, can better manage and prevent catastrophic tail-end risks, moving beyond traditional risk-neutral objectives.
The proposed Bellman operator for CVaR MDPs offers a more robust method for designing AI agents that can explicitly account for and mitigate extreme negative outcomes, rather than simply optimizing for average performance.
- · AI developers
- · Safety-critical industries (e.g., autonomous vehicles, healthcare, finance)
- · AI ethics and safety researchers
- · Traditional risk-neutral AI models
- · Sectors reliant on less robust risk management frameworks
Improved reliability and safety guarantees for AI systems deployed in high-stakes environments.
Accelerated adoption of AI in domains previously hesitant due to unaddressed catastrophic risk concerns.
Potential for new regulatory frameworks and compliance standards to incorporate CVaR-aware AI capabilities.
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Read at arXiv cs.LG