Multi-agent reinforcement learning for Markov routing games: A new modeling paradigm for dynamic traffic assignment

A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

(2021) Xuan Di et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Ridesharing user equilibrium with nodal matching cost and its implications for congestion tolling and platform pricing

(2021) Xu Chen et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Dynamic driving and routing games for autonomous vehicles on networks: A mean field game approach

(2021) Kuang Huang et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Reward design for driver repositioning using multi-agent reinforcement learning

(2020) Zhenyu Shou et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

(2020) Thanh Thi Nguyen et al.   IEEE Transactions on Cybernetics

Superhuman AI for multiplayer poker

(2019) Noam Brown et al.   SCIENCE

Grandmaster level in StarCraft II using multi-agent reinforcement learning

(2019) Oriol Vinyals et al.   NATURE

A unified equilibrium framework of new shared mobility systems

(2019) Xuan Di et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

Optimal passenger-seeking policies on E-hailing platforms using Markov decision process and imitation learning

(2019) Zhenyu Shou et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

A link-node reformulation of ridesharing user equilibrium with network design

(2018) Xuan Di et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

Analysing the impact of travel information for minimising the regret of route choice

(2018) Gabriel de O. Ramos et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network

(2018) Chao Mao et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

The mathematical foundations of dynamic user equilibrium

(2018) Terry L. Friesz et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

Superhuman AI for heads-up no-limit poker: Libratus beats top professionals

(2017) Noam Brown et al.   SCIENCE

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

(2016) Gitae Kim et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Stochastic games

(2015) Eilon Solan et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Continuous-time dynamic system optimum for single-destination traffic networks with queue spillbacks

(2014) Rui Ma et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems

(2012) Laetitia Matignon et al.   KNOWLEDGE ENGINEERING REVIEW

Continuous-time point-queue models in dynamic network loading

(2012) Xuegang (Jeff) Ban et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

Dynamic user equilibrium based on a hydrodynamic model

(2012) Terry L. Friesz et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

A cell-based Merchant–Nemhauser model for the system optimum dynamic traffic assignment problem

(2010) Yu (Marco) Nie   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL