Reinforcement learning for ridesharing: An extended survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to decision optimization problems in a typical ridesharing system …
learning approaches to decision optimization problems in a typical ridesharing system …
A systematic literature review on machine learning in shared mobility
J Teusch, JN Gremmel, C Koetsier… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Shared mobility has emerged as a sustainable alternative to both private transportation and
traditional public transport, promising to reduce the number of private vehicles on roads …
traditional public transport, promising to reduce the number of private vehicles on roads …
Hindsight learning for mdps with exogenous inputs
Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …
Learning and information in stochastic networks and queues
We review the role of information and learning in the stability and optimization of queueing
systems. In recent years, techniques from supervised learning, online learning, and …
systems. In recent years, techniques from supervised learning, online learning, and …
Reinforcement learning for ridesharing: A survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to ridesharing problems. Papers on the topics of rideshare matching …
learning approaches to ridesharing problems. Papers on the topics of rideshare matching …
Graph meta-reinforcement learning for transferable autonomous mobility-on-demand
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to
existing transportation paradigms, currently challenged by urbanization and increasing …
existing transportation paradigms, currently challenged by urbanization and increasing …
Neural inventory control in networks via hindsight differentiable policy optimization
Inventory management offers unique opportunities for reliably evaluating and applying deep
reinforcement learning (DRL). Rather than evaluate DRL algorithms by comparing against …
reinforcement learning (DRL). Rather than evaluate DRL algorithms by comparing against …
Deep reinforcement learning for demand driven services in logistics and transportation systems: A survey
Recent technology development brings the booming of numerous new Demand-Driven
Services (DDS) into urban lives, including ridesharing, on-demand delivery, express …
Services (DDS) into urban lives, including ridesharing, on-demand delivery, express …
[HTML][HTML] Scalable policies for the dynamic traveling multi-maintainer problem with alerts
P Verleijsdonk, W van Jaarsveld… - European Journal of …, 2024 - Elsevier
Downtime of industrial assets such as wind turbines and medical imaging devices is costly.
To avoid such downtime costs, companies seek to initiate maintenance just before failure …
To avoid such downtime costs, companies seek to initiate maintenance just before failure …
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 …
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …