Fetching the paper…
Reading the bibliography…
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP).
An integer programming approach to the vehicle scheduling problem
Brian A Foster and David M Ryan · 1976
Earlier work this paper cites.
Classification in vehicle routing and scheduling
Lawrence Bodin and Bruce Golden · 1981
Earlier work this paper cites.
A parallel route building algorithm for the vehicle routing and scheduling problem with time windows
Jean-Yves Potvin and Jean-Marc Rousseau · 1993
Earlier work this paper cites.
Using constraint programming and local search methods to solve vehicle routing problems
Paul Shaw · 1998
Earlier work this paper cites.
Cooperative pathfinding
David Silver · 2005
Earlier work this paper cites.
The vehicle rescheduling problem: Model and algorithms
Jing-Quan Li, Pitu B Mirchandani, and Denis Borenstein · 2007
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Reinforcement learning-based multi-agent system for network traffic signal control
Itamar Arel, Cong Liu, Tom Urbanik, and Airton G Kohls · 2010
Earlier work this paper cites.
SIPP: Safe interval path planning for dynamic environments
Mike Phillips and Maxim Likhachev · 2011
Earlier work this paper cites.
Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
Earlier work this paper cites.
Dueling network architectures for deep reinforcement learning, 2016
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, and Nando de Freitas · 2016
Earlier work this paper cites.
Cooperative multi-agent control using deep reinforcement learning
Jayesh K. Gupta, Maxim Egorov, and Mykel Kochenderfer · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, YI WU, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Multi-agent path finding with delay probabilities
Hang Ma, T. K. Satish Kumar, and Sven Koenig · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
The future of swiss railway dispatching. deep learning and simulation on dgx-1, 3 2018
Adrian Egli, Erik Nygren, Beat Wettstein, Lothar Jöckel, and Dirk Abels · 2018
Cited alongside, same era.
Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
Cited alongside, same era.
Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado Van Hasselt, and David Silver · 2018
Cited alongside, same era.
Learning attentional communication for multi-agent cooperation
Jiechuan Jiang and Zongqing Lu · 2018
Cited alongside, same era.
The starcraft multi-agent challenge, 2019
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, and Shimon Whiteson · 2019
Later among the works it cites.
Primal: Pathfinding via reinforcement and imitation multi-agent learning
Guillaume Sartoretti, Justin Kerr, Yunfei Shi, Glenn Wagner, TK Satish Kumar, Sven Koenig, and Howie Choset · 2019
Later among the works it cites.
QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
Later among the works it cites.
Multi-agent pathfinding: Definitions, variants, and benchmarks
Roni Stern, Nathan R. Sturtevant, Ariel Felner, Sven Koenig, Hang Ma, Thayne Walker, Jiaoyang Li, Dor Atzmon, Liron Cohen, T. K. Satish Kumar, Eli Boyarski, and Roman Bartak · 2019
Later among the works it cites.
Online multi-agent pathfinding
Jirí Svancara, Marek Vlk, Roni Stern, Dor Atzmon, and Roman Barták · 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…
Junqi Jin, Chengru Song, Han Li, Kun Gai, Jun Wang, and Weinan Zhang · 2018
Cited alongside, same era.
Rllib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica · 2018
Cited alongside, same era.
Multi-agent path finding with deadlines
Hang Ma, Glenn Wagner, Ariel Felner, Jiaoyang Li, T. K. Satish Kumar, and Sven Koenig · 2018
Cited alongside, same era.
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
Cited alongside, same era.
Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, and Thore Graepel · 2018
Cited alongside, same era.
Exponentially weighted imitation learning for batched historical data
Qing Wang, Jiechao Xiong, Lei Han, peng sun, Han Liu, and Tong Zhang · 2018
Cited alongside, same era.
TarMAC: Targeted multi-agent communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau · 2019
Cited alongside, same era.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander S. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
Later among the works it cites.
Probabilistic recursive reasoning for multi-agent reinforcement learning
Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, and Wei Pan · 2019
Later among the works it cites.
Probabilistic robust multi-agent path finding
Dor Atzmon, Roni Stern, Ariel Felner, Nathan R. Sturtevant, and Sven Koenig · 2020
Later among the works it cites.
Emergent tool use from multi-agent autocurricula, 2020
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2020
Later among the works it cites.
Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
Later among the works it cites.
Scalable rail planning and replanning: Winning the 2020 flatland challenge
Jiaoyang Li, Zhe chen, Yi Zheng, Shao-Hung Chan, Daniel Harabor, Peter J. Stuckey, Hang Ma, and Sven Koenig · 2020
Later among the works it cites.
Flatland-rl : Multi-agent reinforcement learning on trains, 2020
Sharada Mohanty, Erik Nygren, Florian Laurent, Manuel Schneider, Christian Scheller, Nilabha Bhattacharya, Jeremy Watson, Adrian Egli, Christian Eichenberger, Christian Baumberger, Gereon Vienken, Irene Sturm, Guillaume Sartoretti, and Giacomo Spigler · 2020
Later among the works it cites.
Weighted qmix: Expanding monotonic value function factorisation
Tabish Rashid, Gregory Farquhar, Bei Peng, and Shimon Whiteson · 2020
Later among the works it cites.
Cooperative deep reinforcement learning for large-scale traffic grid signal control
T. Tan, F. Bao, Y. Deng, A. Jin, Q. Dai, and J. Wang · 2020
Later among the works it cites.
Learning nearly decomposable value functions via communication minimization
Tonghan Wang, Jianhao Wang, Chongyi Zheng, and Chongjie Zhang · 2020
Later among the works it cites.
PRIMAL 2 : Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Lifelong
Mehul Damani, Zhiyao Luo, Emerson Wenzel, and Guillaume Sartoretti · 2021
Closest in time.