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Prioritization Methods for Accelerating MDP Solvers
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Topological value iteration algorithm for Markov decision processes
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Prioritizing point-based POMDP solvers
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Over the past few years, point-based POMDP solvers scaled up to produce approximate solutions to mid-sized domains. However, to solve real world problems, solvers must exploit the structure of the domain. In this paper we focus on the topological structure of the problem, where the state space contains layers of states. We present here the Topological Order Planner (TOP) that utilizes the topological structure of the domain to compute belief space trajectories. TOP rapidly produces trajectories focused on the solveable regions of the belief space, thus reducing the number of redundant backups considerably. We demonstrate TOP to produce good quality policies faster than any other pointbased algorithm on domains with sufficient structure.