報酬拡張強化学習による精密自動駐車の連続制御
Reward-Augmented Reinforcement Learning for Continuous Control in Precision Autonomous Parking via Policy Optimization Methods
高忠実度3Dシミュレータ上で、3種類の報酬設計とon/off-policy最適化を組み合わせ、自動駐車の連続制御を強化学習で実現。マイルストーン拡張報酬が91%の成功率を達成。
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著者: Ahmad Suleman, Misha Urooj Khan, Zeeshan Kaleem, Ali H. Alenezi, Iqra Shabbir, Sinem Coleri, Chau Yuen
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Autonomous parking (AP) represents a critical yet complex subset of intelligent vehicle automation, characterized by tight spatial constraints, frequent close-range obstacle interactions, and stringent safety margins. However, conventional rule-based and model-predictive methods often lack the adaptability and generalization needed to handle the nonlinear and environment-dependent complexities of AP. To address these limitations, we propose a reward-augmented learning framework for AP (RARLAP), that mitigates the inherent complexities of continuous-domain control by leveraging structured reward design to induce smooth and adaptable policy behavior, trained entirely within a high-fidelity Unity-based custom 3D simulation environment. We systematically design and assess three structured reward strategies: goal-only reward (GOR), dense proximity reward (DPR), and milestone-augmented reward (MAR), each integrated with both on-policy and off-policy optimization paradigms. Empirical evaluations demonstrate that the on-policy MAR achieves a 91\% success rate, yielding smoother trajectories and more robust behavior, while GOR and DPR fail to guide effective learning. Convergence and trajectory analyses demonstrate that the proposed framework enhances policy adaptability, accelerates training, and improves safety in continuous control. Overall, RARLAP establishes that reward augmentation effectively addresses complex autonomous parking challenges, enabling scalable and efficient policy optimization with both on- and off-policy methods. To support reproducibility, the code accompanying this paper is publicly available.
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