  Decision making under uncertainty can be framed as a partially observable
Markov decision process (POMDP). Finding exact solutions of POMDPs is generally
computationally intractable, but the solution can be approximated by
sampling-based approaches. These sampling-based POMDP solvers rely on
multi-armed bandit (MAB) heuristics, which assume the outcomes of different
actions to be uncorrelated. In some applications, like motion planning in
continuous spaces, similar actions yield similar outcomes. In this paper, we
utilize variants of MAB heuristics that make Lipschitz continuity assumptions
on the outcomes of actions to improve the efficiency of sampling-based planning
approaches. We demonstrate the effectiveness of this approach in the context of
motion planning for automated driving.
