Fetching the paper…
Reading the bibliography…
Reinforcement learning (RL) is a class of artificial intelligence algorithms being used to design adaptive optimal controllers through online learning.
J. Sherman and W. J. Morrison, “Adjustment of an inverse matrix corresponding to a change in one element of a given matrix,” The Annals of Mathematical Statistics
1950
Earlier work this paper cites.
E. W. Dijkstra et al
1959
Earlier work this paper cites.
A. A. Stoorvogel and A. J. Weeren, “The discrete-time riccati equation related to the h/sub/spl infin//control problem,” IEEE Transactions on Automatic Control
1994
Earlier work this paper cites.
T. Baar and P. Bernhard, “If’-optimal control and related minimax design problems,” Birkh/iuser,
1995
Earlier work this paper cites.
A. Al-Tamimi, F. L. Lewis, and M. Abu-Khalaf, “Model-free q-learning designs for linear discrete-time zero-sum games with application to h-infinity control,” Automatica
2007
Earlier work this paper cites.
M. Volkov, J. Aslam, and D. Rus, “Markov-based redistribution policy model for future urban mobility networks,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems
2012
Earlier work this paper cites.
M. Pavone, S. L. Smith, E. Frazzoli, and D. Rus, “Robotic load balancing for mobility-on-demand systems,” The International Journal of Robotics Research
2012
Earlier work this paper cites.
H.-N. Wu and B. Luo, “Neural network based online simultaneous policy update algorithm for solving the hji equation in nonlinear h ∞
2012
Earlier work this paper cites.
F. L. Lewis, D. Vrabie, and K. G. Vamvoudakis, “Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers,” IEEE Control Systems Magazine
2012
Earlier work this paper cites.
Stanford University, 2016
R. Zhang, Models and Large-scale Coordination Algorithms for Autonomous Mobility-on-demand · 2016
Earlier work this paper cites.
R. Zhang and M. Pavone, “Control of robotic mobility-on-demand systems: a queueing-theoretical perspective,” The International Journal of Robotics Research
2016
Earlier work this paper cites.
J. Wen, J. Zhao, and P. Jaillet, “Rebalancing shared mobility-on-demand systems: A reinforcement learning approach,” in 2017 IEEE 20th international conference on intelligent transportation systems (ITSC)
2017
Earlier work this paper cites.
B. Kiumarsi, F. L. Lewis, and Z.-P. Jiang, “H ∞
2017
Cited alongside, same era.
Y. Gao, D. Jiang, and Y. Xu, “Optimize taxi driving strategies based on reinforcement learning,” International Journal of Geographical Information Science
2018
Cited alongside, same era.
K. Lin, R. Zhao, Z. Xu, and J. Zhou, “Efficient large-scale fleet management via multi-agent deep reinforcement learning,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
2018
Cited alongside, same era.
M. Guériau and I. Dusparic, “Samod: Shared autonomous mobility-on-demand using decentralized reinforcement learning,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
2018
Cited alongside, same era.
F. Rossi, R. Zhang, Y. Hindy, and M. Pavone, “Routing autonomous vehicles in congested transportation networks: Structural properties and coordination algorithms,” Autonomous Robots
M. Tsao, D. Milojevic, C. Ruch, M. Salazar, E. Frazzoli, and M. Pavone, “Model predictive control of ride-sharing autonomous mobility-on-demand systems,” in 2019 International Conference on Robotics and Automation (ICRA)
2019
Later among the works it cites.
Y. Abbasi-Yadkori, N. Lazic, and C. Szepesvári, “Model-free linear quadratic control via reduction to expert prediction,” in The 22nd International Conference on Artificial Intelligence and Statistics
2019
Later among the works it cites.
N. Matni, A. Proutiere, A. Rantzer, and S. Tu, “From self-tuning regulators to reinforcement learning and back again,” in 2019 IEEE 58th Conference on Decision and Control (CDC)
2019
Later among the works it cites.
K. Zhang, Z. Yang, and T. Basar, “Policy optimization provably converges to nash equilibria in zero-sum linear quadratic games,” Advances in Neural Information Processing Systems
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
R. Iglesias, F. Rossi, K. Wang, D. Hallac, J. Leskovec, and M. Pavone, “Data-driven model predictive control of autonomous mobility-on-demand systems,” in 2018 IEEE international conference on robotics and automation (ICRA)
2018
Cited alongside, same era.
MIT press, 2018
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction · 2018
Cited alongside, same era.
S. Tu and B. Recht, “Least-squares temporal difference learning for the linear quadratic regulator,” in International Conference on Machine Learning
2018
Cited alongside, same era.
M. Fazel, R. Ge, S. Kakade, and M. Mesbahi, “Global convergence of policy gradient methods for the linear quadratic regulator,” in International Conference on Machine Learning
2018
Cited alongside, same era.
C. Fluri, C. Ruch, J. Zilly, J. Hakenberg, and E. Frazzoli, “Learning to operate a fleet of cars,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
2019
Cited alongside, same era.
A. O. Al-Abbasi, A. Ghosh, and V. Aggarwal, “Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems
2019
Cited alongside, same era.
A. Carron, F. Seccamonte, C. Ruch, E. Frazzoli, and M. N. Zeilinger, “Scalable model predictive control for autonomous mobility-on-demand systems,” IEEE Transactions on Control Systems Technology
2019
Cited alongside, same era.
B. Turan, R. Pedarsani, and M. Alizadeh, “Dynamic pricing and fleet management for electric autonomous mobility on demand systems,” Transportation Research Part C: Emerging Technologies
2020
Later among the works it cites.
Z. Lei, X. Qian, and S. V. Ukkusuri, “Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks,” Transportation Research Part C: Emerging Technologies
2020
Later among the works it cites.
S. Dean, H. Mania, N. Matni, B. Recht, and S. Tu, “On the sample complexity of the linear quadratic regulator,” Foundations of Computational Mathematics
2020
Later among the works it cites.
A. Goel, A. L. Bruce, and D. S. Bernstein, “Recursive least squares with variable-direction forgetting: Compensating for the loss of persistency [lecture notes],” IEEE Control Systems Magazine
2020
Later among the works it cites.
E. Skordilis, Y. Hou, C. Tripp, M. Moniot, P. Graf, and D. Biagioni, “A modular and transferable reinforcement learning framework for the fleet rebalancing problem,” IEEE Transactions on Intelligent Transportation Systems
2021
Later among the works it cites.
A. Rantzer, “Minimax adaptive control for a finite set of linear systems,” in Learning for Dynamics and Control
2021
Later among the works it cites.
G. Guo and Y. Xu, “A deep reinforcement learning approach to ride-sharing vehicle dispatching in autonomous mobility-on-demand systems,” IEEE Intelligent Transportation Systems Magazine
2022
Later among the works it cites.
D. Q. Nguyen-Phuoc, M. Zhou, M. H. Chua, A. R. Alho, S. Oh, R. Seshadri, and D.-T. Le, “Examining the effects of automated mobility-on-demand services on public transport systems using an agent-based simulation approach,” Transportation Research Part A: Policy and Practice
2023
Closest in time.