確率的報酬とオンライン推論による多様な人間の運転行動の表現
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
人間の運転行動の多様性を捉えるため、連続領域でコスト関数の分布を直接学習する確率的逆強化学習フレームワークを提案し、実運転データで多様な運転スタイルの抽出に成功した。
著者: Liting Sun, Zheng Wu, Hengbo Ma, Masayoshi Tomizuka
分類: cs.RO, cs.AI
原文アブストラクト
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Cost/reward learning, as an efficient way to learn and represent human behavior, has been successfully applied in many domains. Most of traditional inverse reinforcement learning (IRL) algorithms, however, cannot adequately capture the diversity of human behavior since they assume that all behavior in a given dataset is generated by a single cost function.In this paper, we propose a probabilistic IRL framework that directly learns a distribution of cost functions in continuous domain. Evaluations on both synthetic data and real human driving data are conducted. Both the quantitative and subjective results show that our proposed framework can better express diverse human driving behaviors, as well as extracting different driving styles that match what human participants interpret in our user study.
