[Submitted on 26 Sep 2026]
Title:Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees
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Abstract:Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost states. Existing risk-averse methods apply static or dynamic Conditional Value at Risk (CVaR) to the value function, capturing trajectory-level risk, but share two gaps: (i) by retaining the immediate cost as an expectation of a state-dependent cost over the belief, the risk \emph{within} the belief is left unaddressed; and (ii) by modifying the value function, they require new tailored algorithms rather than reusing existing expectation-based planners. We instead apply CVaR to the immediate cost over the belief at each step, directly targeting per-step uncertainty about the current state. The standard expected cumulative return is retained as the objective, so the resulting problem has a standard MDP structure: any expectation-based POMDP planner can be made risk-sensitive by changing only the cost computation. We inherit finite-time guarantees for policy evaluation and sparse sampling---with estimation error independent of the risk level---and, as our central theoretical result, prove a finite-time bound on the gap between the particle belief MDP surrogate and the original POMDP, which together yield an end-to-end guarantee from the true POMDP value to the algorithmic estimate. In the risk-neutral limit, the formulation recovers standard expectation-based planning.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.35874 [cs.AI]
(or arXiv:2609.35874v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.35874
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yaacov Pariente [view email] [v1] Sat, 26 Sep 2026 18:37:28 UTC (132 KB)
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