自律走行レース向け模倣学習ベース制御ポリシーのベンチマーク比較
A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing
スケールカーの自律レースを対象に、複数の模倣学習ポリシーをシミュレーションと実機で比較し、対話型模倣学習が従来手法を上回り、強化学習のブートストラップとしても有効であることを示した。
著者: Xiatao Sun, Mingyan Zhou, Zhijun Zhuang, Shuo Yang, Johannes Betz, Rahul Mangharam
分類: cs.RO
原文アブストラクト
Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driving at the limits of the vehicle's handling. To train agile control policies for autonomous racing, learning-based approaches largely utilize reinforcement learning, albeit with mixed results. In this study, we benchmark a variety of imitation learning policies for racing vehicles that are applied directly or for bootstrapping reinforcement learning both in simulation and on scaled real-world environments. We show that interactive imitation learning techniques outperform traditional imitation learning methods and can greatly improve the performance of reinforcement learning policies by bootstrapping thanks to its better sample efficiency. Our benchmarks provide a foundation for future research on autonomous racing using Imitation Learning and Reinforcement Learning.
