# Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning

## TL;DR

This paper demonstrates that offline reinforcement learning can effectively control autonomous mobility-on-demand systems using only offline data, matching online methods and enabling efficient fine-tuning without complex simulations.

## Contribution

It introduces the application of offline RL to AMoD control, showing it achieves comparable performance to online methods and reduces reliance on simulation environments.

## Key findings

- Offline RL achieves performance comparable to online methods.
- Offline RL enables sample-efficient online fine-tuning.
- Offline RL eliminates the need for complex simulation environments.

## Abstract

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.