リプシッツバンディットを用いた連続空間のPOMDPにおける効率的サンプリング
Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces
行動の結果がリプシッツ連続であると仮定するバンディット手法をPOMDPのサンプリングベース計画に導入し、自動運転の動作計画で効率化を実証した論文。
著者: Ömer Şahin Taş, Felix Hauser, Martin Lauer
分類: cs.RO, cs.LG
原文アブストラクト
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.
