Fetching the paper…
Reading the bibliography…
A challenge in multi-agent reinforcement learning is to be able to generalize over intractable state-action spaces.
Foundations of the parafac procedure: Models and conditions for an "explanatory" multi-modal factor analysis
Richard Harshman · 1970
Earlier work this paper cites.
Qplex: Duplex dueling multi-agent q-learning
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang · 2008
Earlier work this paper cites.
Rode: Learning roles to decompose multi-agent tasks
Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, and Chongjie Zhang · 2010
Earlier work this paper cites.
A method of moments for mixture models and hidden markov models
Animashree Anandkumar, Daniel Hsu, and Sham M. Kakade · 2012
Earlier work this paper cites.
A tensor factorization approach to generalization in multi-agent reinforcement learning
Stefano Bromuri · 2012
Earlier work this paper cites.
Most tensor problems are np-hard
Christopher J. Hillar and Lek-Heng Lim · 2013
Earlier work this paper cites.
Provable tensor factorization with missing data
Prateek Jain and Sewoong Oh · 2014
Earlier work this paper cites.
Tensor decompositions for learning latent variable models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, and Matus Telgarsky · 2014
Earlier work this paper cites.
Guaranteed non-orthogonal tensor decomposition via alternating rank- 1 1 updates, 2015
Animashree Anandkumar, Rong Ge, and Majid Janzamin · 2015
Cited alongside, same era.
Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives
Andrzej Cichocki, A. Phan, Qibin Zhao, Namgil Lee, I. Oseledets, Masashi Sugiyama, and Danilo P. Mandic · 2017
Cited alongside, same era.
A survey of model compression and acceleration for deep neural networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2017
Cited alongside, same era.
Reinforcement learning of pomdps using spectral methods
Kamyar Azizzadenesheli, Alessandro Lazaric, and Animashree Anandkumar · 2017
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
T-net: Parametrizing fully convolutional nets with a single high-order tensor
Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, and Maja Pantic · 2019
Later among the works it cites.
Uneven: Universal value exploration for multi-agent reinforcement learning
Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Böhmer, and Shimon Whiteson · 2020
Later among the works it cites.
Tensor completion made practical
Allen Liu and Ankur Moitra · 2020
Later among the works it cites.
Factorized higher-order cnns with an application to spatio-temporal emotion estimation
Jean Kossaifi, Antoine Toisoul, Adrian Bulat, Yannis Panagakis, Timothy M. Hospedales, and Maja Pantic · 2020
Later among the works it cites.
Incremental multi-domain learning with network latent tensor factorization
Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos, and Maja Pantic · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Maven: Multi-agent variational exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson · 2019
Cited alongside, same era.
Tesseract: Tensorised actors for multi-agent reinforcement learning
Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, and Animashree Anandkumar · 2021
Closest in time.
Open-ended learning leads to generally capable agents
DeepMind-OEL, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, and Wojciech Marian Czarnecki · 2021
Closest in time.