視覚に基づく深層模倣強化学習による自動運転レース
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
模倣学習とモデルベース強化学習を組み合わせ、視覚入力のみでアジャイルな自動運転レースを実現する手法を提案し、シミュレーションと実機の小型RCカーで有効性を示した。
著者: Peide Cai, Hengli Wang, Huaiyang Huang, Yuxuan Liu, Ming Liu
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance. Demonstration videos are available at https://caipeide.github.io/autorace-dirl/
