730 research outputs found
Rebalancing shared mobility systems by user incentive scheme via reinforcement learning
Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, leading to users being unable to receive service. If such imbalance problems are not mitigated some users will not be serviced. There is an increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to produce an effective user incentivization policy scheme to encourage users of a shared mobility system to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve user experience. In this thesis, two important questions are explored: (1) What state-action representation should be used to produce an effective user incentive scheme for a shared mobility system? (2) How effective are reinforcement learning-based solutions on the rebalancing problem under varying levels of resource supply, user demand, and budget? Our extensive empirical results based on data-driven simulation show that: 1. A state space with predicted user behavior coupled with a simple action mechanism produces an effective incentive scheme under varying environment scenarios. 2. The reinforcement learning-based incentive mechanisms perform at varying degrees of effectiveness under different environmental scenarios in terms of service level
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
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
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