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Decision making under uncertainty can be framed as a partially observableMarkov decision process (POMDP). Finding exact solutions of POMDPs is generallycomputationally intractable, but the solution can be approximated bysampling-based approaches. These sampling-based POMDP solvers rely onmulti-armed bandit (MAB) heuristics, which assume the outcomes of differentactions to be uncorrelated. In some applications, like motion planning incontinuous spaces, similar actions yield similar outcomes. In this paper, weutilize variants of MAB heuristics that make Lipschitz continuity assumptionson the outcomes of actions to improve the efficiency of sampling-based planningapproaches. We demonstrate the effectiveness of this approach in the context ofmotion planning for automated driving.

Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces