シナリオベース階層強化学習による自動運転意思決定
Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making
階層強化学習とシナリオベース環境を統合し、自動運転の意思決定を効率的かつ安全に学習するフレームワークSAD-RLを提案した。
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著者: M. Youssef Abdelhamid, Lennart Vater, Zlatan Ajanovic
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Developing decision-making algorithms for highly automated driving systems remains challenging, since these systems have to operate safely in an open and complex environments. Reinforcement Learning (RL) approaches can learn comprehensive decision policies directly from experience and already show promising results in simple driving tasks. However, current approaches fail to achieve generalizability for more complex driving tasks and lack learning efficiency. Therefore, we present Scenario-based Automated Driving Reinforcement Learning (SAD-RL), the first framework that integrates Reinforcement Learning (RL) of hierarchical policy in a scenario-based environment. A high-level policy selects maneuver templates that are evaluated and executed by a low-level control logic. The scenario-based environment allows to control the training experience for the agent and to explicitly introduce challenging, but rate situations into the training process. Our experiments show that an agent trained using the SAD-RL framework can achieve safe behaviour in easy as well as challenging situations efficiently. Our ablation studies confirmed that both HRL and scenario diversity are essential for achieving these results.
