階層型計画と強化学習によるマルチレーン自動走行
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning
行動決定・経路計画・制御の階層に状態行動空間の抽象化を組み込み、強化学習で複数車線の自動走行を実現し、シミュレーション間のモデル転移も示した論文。
著者: Kasra Rezaee, Peyman Yadmellat, Masoud S. Nosrati, Elmira Amirloo Abolfathi, Mohammed Elmahgiubi, Jun Luo
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lane cruising by combining a hierarchical reinforcement learning framework with a novel state-action space abstraction. While the proposed solution follows the classical hierarchy of behavior decision, motion planning and control, it introduces a key intermediate abstraction within the motion planner to discretize the state-action space according to high level behavioral decisions. We argue that this design allows principled modular extension of motion planning, in contrast to using either monolithic behavior cloning or a large set of hand-written rules. Moreover, we demonstrate that our state-action space abstraction allows transferring of the trained models without retraining from a simulated environment with virtually no dynamics to one with significantly more realistic dynamics. Together, these results suggest that our proposed hierarchical architecture is a promising way to allow reinforcement learning to be applied to complex multi-lane cruising in the real world.
