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[2602.23073] Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds
Summary
This paper presents a theoretical framework for accelerating risk-averse policy evaluation in partially observable Markov decision processes (POMDPs), focusing on Conditional Value-at-Risk (CVaR) with performance guarantees.
Why It Matters
The research addresses a critical challenge in artificial intelligence: making reliable decisions under uncertainty. By improving the efficiency of policy evaluation in POMDPs, this work has implications for the development of safer autonomous agents, enhancing their decision-making capabilities in complex environments.
Key Takeaways
- Introduces a theoretical framework for accelerated CVaR evaluation in POMDPs.
- Establishes new bounds on CVaR using auxiliary random variables, enhancing interpretability.
- Develops estimators for CVaR bounds within a particle-belief MDP framework.
- Demonstrates substantial computational speedups while ensuring policy safety.
- Empirical evaluations confirm the effectiveness of the proposed methods across multiple domains.
Mathematics > Statistics Theory arXiv:2602.23073 (math) [Submitted on 26 Feb 2026] Title:Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds Authors:Yaacov Pariente, Vadim Indelman View a PDF of the paper titled Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds, by Yaacov Pariente and 1 other authors View PDF HTML (experimental) Abstract:Risk-averse decision-making under uncertainty in partially observable domains is a central challenge in artificial intelligence and is essential for developing reliable autonomous agents. The formal framework for such problems is the partially observable Markov decision process (POMDP), where risk sensitivity is introduced through a risk measure applied to the value function, with Conditional Value-at-Risk (CVaR) being a particularly significant criterion. However, solving POMDPs is computationally intractable in general, and approximate methods rely on computationally expensive simulations of future agent trajectories. This work introduces a theoretical framework for accelerating CVaR value function evaluation in POMDPs with formal performance guarantees. We derive new bounds on the CVaR of a random variable X using an auxiliary random variable Y, under assumptions relating their cumulative distribution and density functions; these bounds yield interpretable concentration inequalities and converge as the distributional discr...