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Optimal decision-making presents a significant challenge for autonomoussystems operating in uncertain, stochastic and time-varying environments.Environmental variability over time can significantly impact the system'soptimal decision making strategy for mission completion. To model suchenvironments, our work combines the previous notion of Time-Varying MarkovDecision Processes (TVMDP) with partial observability and introducesTime-Varying Partially Observable Markov Decision Processes (TV-POMDP). Wepropose a two-pronged approach to accurately estimate and plan within theTV-POMDP: 1) Memory Prioritized State Estimation (MPSE), which leveragesweighted memory to provide more accurate time-varying transition estimates; and2) an MPSE-integrated planning strategy that optimizes long-term rewards whileaccounting for temporal constraint. We validate the proposed framework andalgorithms using simulations and hardware, with robots exploring a partiallyobservable, time-varying environments. Our results demonstrate superiorperformance over standard methods, highlighting the framework's effectivenessin stochastic, uncertain, time-varying domains.

Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment