Fetching the paper…
Reading the bibliography…
Multi-agent reinforcement learning tasks put a high demand on the volume of training samples.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Massively parallel methods for deep reinforcement learning
Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, et al · 2015
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.
A concise introduction to decentralized POMDPs , volume 1
Frans A Oliehoek, Christopher Amato, et al · 2016
Earlier work this paper cites.
Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2017
Earlier work this paper cites.
Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, SM Eslami, et al · 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.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
Cited alongside, same era.
Starcraft ii: A new challenge for reinforcement learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, et al · 2017
Cited alongside, same era.
Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 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.
Recurrent experience replay in distributed reinforcement learning
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
Later among the works it cites.
Seed rl: Scalable and efficient deep-rl with accelerated central inference
Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, and Marcin Michalski · 2019
Later among the works it cites.
The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson · 2019
Later among the works it cites.
Google research football: A novel reinforcement learning environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, et al · 2020
Later among the works it cites.
Roma: Multi-agent reinforcement learning with emergent roles
Tonghan Wang, Heng Dong, Victor Lesser, and Chongjie Zhang · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, and Will Dabney · 2018
Cited alongside, same era.
Openai five
OpenAI · 2018
Cited alongside, same era.
Qplex: Duplex dueling multi-agent q-learning
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang
Cited in the paper.
Rode: Learning roles to decompose multi-agent tasks
Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, and Chongjie Zhang
Cited in the paper.
Later among the works it cites.
Celebrating diversity in shared multi-agent reinforcement learning
Chenghao Li, Chengjie Wu, Tonghan Wang, Jun Yang, Qianchuan Zhao, and Chongjie Zhang · 2021
Closest in time.