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We propose a solution to a time-varying variant of Markov Decision Processeswhich can be used to address decision-theoretic planning problems forautonomous systems operating in unstructured outdoor environments. We explorethe time variability property of the planning stochasticity and investigate thestate reachability, based on which we then develop an efficient iterativemethod that offers a good trade-off between solution optimality and timecomplexity. The reachability space is constructed by analyzing the means andvariances of states' reaching time in the future. We validate our algorithmthrough extensive simulations using ocean data, and the results show that ourmethod achieves a great performance in terms of both solution quality andcomputing time.

Reachable Space Characterization of Markov Decision Processes with Time Variability