Fetching the paper…
Reading the bibliography…
This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings.
Q-learning
Watkins, C. J.; and Dayan, P. 1992 · 1992
Earlier work this paper cites.
Multi-agent reinforcement learning: Independent vs. cooperative agents
Tan, M. 1993 · 1993
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Littman, M. L. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S.; McAllester, D.; Singh, S.; and Mansour, Y. 1999 · 1999
Earlier work this paper cites.
Qatten: A general framework for cooperative multiagent reinforcement learning
Yang, Y.; Hao, J.; Liao, B.; Shao, K.; Chen, G.; Liu, W.; and Tang, H. 2020 · 2002
Earlier work this paper cites.
QV (lambda)-learning: A new on-policy reinforcement learning algrithm
Wiering, M. A. 2005 · 2005
Earlier work this paper cites.
Qplex: Duplex dueling multi-agent q-learning
Wang, J.; Ren, Z.; Liu, T.; Yu, Y.; and Zhang, C. 2020 · 2008
Earlier work this paper cites.
The QV family compared to other reinforcement learning algorithms
Wiering, M. A.; and Van Hasselt, H. 2009 · 2009
Earlier work this paper cites.
Solving multi-agent decision problems modeled as dec-pomdp: A robot soccer case study
Aşık, O.; and Akın, H. L. 2012 · 2012
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J.; Gulcehre, C.; Cho, K.; and Bengio, Y. 2014 · 2014
Cited alongside, same era.
Deep recurrent q-learning for partially observable MDPs
Hausknecht, M.; and Stone, P. 2015 · 2015
Cited alongside, same era.
Deep learning
LeCun, Y.; Bengio, Y.; and Hinton, G. 2015 · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; Petersen, S.; Beattie, C.; Sadik, A.; Antonoglou, I.; King, H.; Kumaran, D.; Wierstra, D.; Legg, S.; and Hassabis, D. 2015 · 2015
Cited alongside, same era.
Deep learning in neural networks: An overview
Schmidhuber, J. 2015 · 2015
Cited alongside, same era.
HyperNetworks
Ha, D.; Dai, A.; and Le, Q. V. 2016 · 2016
Cited alongside, same era.
QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning
Rashid, T.; Samvelyan, M.; de Witt, C. S.; Farquhar, G.; Foerster, J.; and Whiteson, S. 2018 · 2018
Later among the works it cites.
Deep quality-value (DQV) learning
Sabatelli, M.; Louppe, G.; Geurts, P.; and Wiering, M. A. 2018 · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Sutton, R. S.; and Barto, A. G. 2018 · 2018
Later among the works it cites.
LIIR: Learning individual intrinsic reward in multi-agent reinforcement learning
Du, Y.; Han, L.; Fang, M.; Liu, J.; Dai, T.; and Tao, D. 2019 · 2019
Later among the works it cites.
Maven: Multi-agent variational exploration
Mahajan, A.; Rashid, T.; Samvelyan, M.; and Whiteson, S. 2019 · 2019
Later among the works it cites.
The starCraft multi-agent challenge
Samvelyan, M.; Rashid, T.; Schroeder de Witt, C.; Farquhar, G.; Nardelli, N.; Rudner, T. G.; Hung, C.-M.; Torr, P. H.; Foerster, J.; and Whiteson, S. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A concise introduction to decentralized POMDPs , volume 1
Oliehoek, F. A.; Amato, C.; et al. 2016 · 2016
Cited alongside, same era.
Rainbow: Combining improvements in deep reinforcement learning
Hessel, M.; Modayil, J.; Van Hasselt, H.; Schaul, T.; Ostrovski, G.; Dabney, W.; Horgan, D.; Piot, B.; Azar, M.; and Silver, D. 2017 · 2017
Cited alongside, same era.
Counterfactual multi-agent policy gradients
Foerster, J. N.; Farquhar, G.; Afouras, T.; Nardelli, N.; and Whiteson, S. 2018 · 2018
Cited alongside, same era.
An introduction to deep reinforcement learning
François-Lavet, V.; Henderson, P.; Islam, R.; Bellemare, M. G.; Pineau, J.; et al. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Son, K.; Kim, D.; Kang, W. J.; Hostallero, D. E.; and Yi, Y. 2019 · 2019
Later among the works it cites.
On multi-agent learning in team sports games
Zhao, Y.; Borovikov, I.; Rupert, J.; Somers, C.; and Beirami, A. 2019 · 2019
Later among the works it cites.
The deep quality-value family of deep reinforcement learning algorithms
Sabatelli, M.; Louppe, G.; Geurts, P.; and Wiering, M. 2020 · 2020
Closest in time.
Deep reinforcement learning with double Q-Learning
Hasselt, H. v.; Guez, A.; and Silver, D. 2016 · 2094
Closest in time.