都市自動運転のためのモデルフリー深層強化学習
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
複雑な都市交差点での自動運転判断を、手設計なしで学習できるモデルフリー深層強化学習フレームワークを提案し、高精細シミュレータのラウンドアバウト課題で有効性を示した。
著者: Jianyu Chen, Bodi Yuan, Masayoshi Tomizuka
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in sub-optimal solutions and is expensive to develop, generalize and maintain at scale. On the other hand, with reinforcement learning (RL), a policy can be learned and improved automatically without any manual designs. However, current RL methods generally do not work well on complex urban scenarios. In this paper, we propose a framework to enable model-free deep reinforcement learning in challenging urban autonomous driving scenarios. We design a specific input representation and use visual encoding to capture the low-dimensional latent states. Several state-of-the-art model-free deep RL algorithms are implemented into our framework, with several tricks to improve their performance. We evaluate our method in a challenging roundabout task with dense surrounding vehicles in a high-definition driving simulator. The result shows that our method can solve the task well and is significantly better than the baseline.
