混雑交通における合流判断：受動的アクタークリティックを用いたマルチポリシー意思決定
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic
強化学習の一種である受動的アクタークリティック（pAC）とマルチポリシー意思決定を組み合わせ、混雑した高速道路で合流先を選ぶ手法を提案し、実交通データで92%の成功率を達成した。
著者: Tomoki Nishi, Prashant Doshi, Danil Prokhorov
分類: cs.AI, cs.RO
原文アブストラクト
Freeway merging in congested traffic is a significant challenge toward fully automated driving. Merging vehicles need to decide not only how to merge into a spot, but also where to merge. We present a method for the freeway merging based on multi-policy decision making with a reinforcement learning method called {\em passive actor-critic} (pAC), which learns with less knowledge of the system and without active exploration. The method selects a merging spot candidate by using the state value learned with pAC. We evaluate our method using real traffic data. Our experiments show that pAC achieves 92\% success rate to merge into a freeway, which is comparable to human decision making.
