Fetching the paper…
Reading the bibliography…
Multi-agent systems exhibit complex behaviors that emanate from the interactions of multiple agents in a shared environment.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
Earlier work this paper cites.
Game theory-based opponent modeling in large imperfect-information games
Sam Ganzfried and Tuomas Sandholm · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Safe opponent exploitation
Sam Ganzfried and Tuomas Sandholm · 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.
Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III · 2016
Earlier work this paper cites.
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
Earlier work this paper cites.
Rl 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2017
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Cited alongside, same era.
Autonomous agents modelling other agents: A comprehensive survey and open problems
Stefano V Albrecht and Peter Stone · 2018
Cited alongside, same era.
Relational forward models for multi-agent learning
Andrea Tacchetti, H Francis Song, Pedro AM Mediano, Vinicius Zambaldi, Neil C Rabinowitz, Thore Graepel, Matthew Botvinick, and Peter W Battaglia · 2018
Later among the works it cites.
Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin Riedmiller · 2018
Later among the works it cites.
Meta-reinforcement learning of structured exploration strategies
Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2018
Later among the works it cites.
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
Later among the works it cites.
Mine: mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and R Devon Hjelm · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Roberta Raileanu, Emily Denton, Arthur Szlam, and Rob Fergus · 2018
Cited alongside, same era.
Machine theory of mind
Neil C Rabinowitz, Frank Perbet, H Francis Song, Chiyuan Zhang, SM Eslami, and Matthew Botvinick · 2018
Cited alongside, same era.
Deep variational reinforcement learning for pomdps
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, and Shimon Whiteson · 2018
Cited alongside, same era.
David Ha and Jürgen Schmidhuber · 2018
Cited alongside, same era.
A deep bayesian policy reuse approach against non-stationary agents
Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, and Changjie Fan · 2018
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz
Cited in the paper.
Learning policy representations in multiagent systems
Aditya Grover, Maruan Al-Shedivat, Jayesh K Gupta, Yura Burda, and Harrison Edwards
Cited in the paper.
Later among the works it cites.
Variational task embeddings for fast adaptation in deep reinforcement learning
Luisa Zintgraf, Maximilian Igl, Kyriacos Shiarlis, Anuj Mahajan, Katja Hofmann, and Shimon Whiteson · 2019
Later among the works it cites.
Agent modeling as auxiliary task for deep reinforcement learning
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E Taylor · 2019
Later among the works it cites.
Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, and Sergey Levine · 2019
Later among the works it cites.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
Later among the works it cites.
Dealing with non-stationarity in multi-agent deep reinforcement learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, and Stefano V Albrecht · 2019
Later among the works it cites.