arXiv:2601.22993v4 Announce Type: replace Abstract: We introduce Canary, a risk-averse method designed to optimize Value-at-Risk (VaR) constrained reinforcement learning (RL) problems. We employ Cantelli's inequality to obtain a tractable, conservative and smooth bound on the VaR constraint based on the first two moments of the cost return. This yields a constraint estimator that remains stable with tight violation thresholds in dense cost regimes. Extending the trust-region framework of the Constrained Policy Optimization (CPO) method, we further provide worst-case bounds for both policy impr
The increasing deployment of AI in safety-critical applications necessitates robust risk management techniques in reinforcement learning, making methods like Canary timely.
This development offers a more robust and predictable way to train AI systems under real-world constraints, crucial for their integration into sensitive operational environments.
AI systems can now be designed with more rigorous guarantees on risk tolerance, shifting from purely performance-driven optimization to include explicit safety and stability considerations.
- · AI developers
- · Safety-critical autonomous systems (e.g., self-driving, industrial robotics)
- · Regulators
- · Developers of unconstrained or high-risk AI applications
- · Systems reliant on ad-hoc risk mitigation strategies
Increased reliability and trustworthiness of AI systems deployed in complex, real-world scenarios.
Faster adoption of AI in industries with stringent safety requirements due to enhanced risk control.
Potential for new regulatory frameworks for AI based on quantifiable risk bounds rather than qualitative assessments.
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