分散型マルチエージェント協調運転のためのエージェント非依存集中学習
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving
単一エージェント強化学習とマスク付き注意ネットワークを用い、エージェント固有の設計を必要とせずに自動運転車の分散協調運転ポリシーを学習する手法を提案し、ボトルネックでの交通流改善を実証した。
著者: Shengchao Yan, Lukas König, Wolfram Burgard
分類: cs.LG, cs.RO
原文アブストラクト
Active traffic management with autonomous vehicles offers the potential for reduced congestion and improved traffic flow. However, developing effective algorithms for real-world scenarios requires overcoming challenges related to infinite-horizon traffic flow and partial observability. To address these issues and further decentralize traffic management, we propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles using single-agent reinforcement learning. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability, eliminating the need for predefined agents or agent-specific experience buffers in multi-agent reinforcement learning. Extensive evaluations across various traffic scenarios demonstrate our method's significant potential in improving traffic flow at critical bottleneck points. Moreover, we address the challenges posed by conservative autonomous vehicle driving behaviors that adhere strictly to traffic rules, showing that our cooperative policy effectively alleviates potential slowdowns without compromising safety.
