動的相互作用を考慮した自動運転強化学習のためのシーン理解
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving
自動運転の意思決定に強化学習を用いるため、Deep Setsやグラフ畳み込みネットワークを拡張したDeep Scenesアーキテクチャを提案し、交通参加者間の相互作用を考慮したシーン表現を学習するGraph-QとDeepScene-Qを開発、SUMOシミュレータで従来手法を上回る性能を示した。
著者: Maria Huegle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component. In this case, leveraging the benefits of deep reinforcement learning for high-level decision making requires special architectures to deal with multiple variable-length sequences of different object types, such as vehicles, lanes or traffic signs. At the same time, the architecture has to be able to cover interactions between traffic participants in order to find the optimal action to be taken. In this work, we propose the novel Deep Scenes architecture, that can learn complex interaction-aware scene representations based on extensions of either 1) Deep Sets or 2) Graph Convolutional Networks. We present the Graph-Q and DeepScene-Q off-policy reinforcement learning algorithms, both outperforming state-of-the-art methods in evaluations with the publicly available traffic simulator SUMO.
