深層強化学習による自動運転交差点管理のための細粒度加速度制御
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
信頼領域ポリシー最適化を用いて自動運転車の加速度を細かく制御し、交差点での全体最適な交通管理を実現する手法を提案した論文。
著者: Hamid Mirzaei, Tony Givargis
分類: cs.AI, cs.RO, cs.SY, eess.SY
原文アブストラクト
Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action/state space dimension requirements. In this paper, we leverage one of the state-of-the-art Reinforcement Learning methods, known as Trust Region Policy Optimization, to tackle intersection management for autonomous vehicles. We show that using this method, we can perform fine-grained acceleration control of autonomous vehicles in a grid street plan to achieve a global design objective.