オフライン強化学習による自律走行車両群の観測ベース制御
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
オフライン強化学習を用いて、実世界のモビリティデータのみから自動運転オンデマンド車両群の制御方策を学習する手法を提案し、オンライン手法と同等の性能と効率的な微調整を実現した。
著者: Carolin Schmidt, Daniele Gammelli, Francisco Camara Pereira, Filipe Rodrigues
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests. The control of these systems is typically formulated as a large network optimization problem, and reinforcement learning (RL) has recently emerged as a promising approach to solve the open challenges in this space. Recent centralized RL approaches focus on learning from online data, ignoring the per-sample-cost of interactions within real-world transportation systems. To address these limitations, we propose to formalize the control of AMoD systems through the lens of offline reinforcement learning and learn effective control strategies using solely offline data, which is readily available to current mobility operators. We further investigate design decisions and provide empirical evidence based on data from real-world mobility systems showing how offline learning allows to recover AMoD control policies that (i) exhibit performance on par with online methods, (ii) allow for sample-efficient online fine-tuning and (iii) eliminate the need for complex simulation environments. Crucially, this paper demonstrates that offline RL is a promising paradigm for the application of RL-based solutions within economically-critical systems, such as mobility systems.
