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arXiv Machine Learning By Valentina Zaccaria, Philip Jordan, Tingting Ni, Gian Antonio Susto, Maryam Kamgarpour

Finite-Time Guarantees for Constrained Reinforcement Learning under Partial Observability

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The paper investigates reinforcement learning in constrained partially observable Markov decision processes (CPOMDPs), where an agent must maximize reward while respecting a cost constraint using incomplete state information. By approximating the CPOMDP with finite-memory models and treating observation-action windows as states of a constrained MDP, the authors develop a primal–dual algorithm that offers the first non-asymptotic guarantees on both average reward suboptimality and constraint violation. Under a filter‑stability assumption, they show that the approximation errors decay geometrically with the window length, and validate their method with experiments in partially observable gridworlds.

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