複数車両の混合現実強化学習による自動運転マルチレーン走行
Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving
混合現実環境で他車や障害物を仮想空間に置き、衝突を安全に学習できるsim2real強化学習フレームワークを提案し、数回の走行で衝突が大幅に減少することを示した。
著者: Rupert Mitchell, Jenny Fletcher, Jacopo Panerati, Amanda Prorok
分類: cs.RO, cs.AI, cs.LG, cs.MA
原文アブストラクト
Autonomous driving promises to transform road transport. Multi-vehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable vehicle interactions. Learning-based methods---such as deep reinforcement learning---are emerging as a promising approach to automatically design intelligent driving policies that can cope with these challenges. Yet, the process of safely learning multi-vehicle driving behaviours is hard: while collisions---and their near-avoidance---are essential to the learning process, directly executing immature policies on autonomous vehicles raises considerable safety concerns. In this article, we present a safe and efficient framework that enables the learning of driving policies for autonomous vehicles operating in a shared workspace, where the absence of collisions cannot be guaranteed. Key to our learning procedure is a sim2real approach that uses real-world online policy adaptation in a mixed-reality setup, where other vehicles and static obstacles exist in the virtual domain. This allows us to perform safe learning by simulating (and learning from) collisions between the learning agent(s) and other objects in virtual reality. Our results demonstrate that, after only a few runs in mixed-reality, collisions are significantly reduced.
