Reachable Space Characterization of Markov Decision Processes with Time Variability
This addresses planning challenges for autonomous systems in dynamic environments, but appears incremental as it builds on existing MDP frameworks with time variability.
The authors tackled the problem of decision-theoretic planning for autonomous systems in unstructured outdoor environments by proposing a solution for time-varying Markov Decision Processes, achieving a good trade-off between solution optimality and time complexity in simulations with ocean data.
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments. We explore the time variability property of the planning stochasticity and investigate the state reachability, based on which we then develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity. The reachability space is constructed by analyzing the means and variances of states' reaching time in the future. We validate our algorithm through extensive simulations using ocean data, and the results show that our method achieves a great performance in terms of both solution quality and computing time.