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This paper concerns imitation learning (IL) (i.e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems.
Inverse reinforcement learning for decentralized non-cooperative multiagent systems. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 1930–1935
Tummalapalli Sudhamsh Reddy, Vamsikrishna Gopikrishna, Gergely Zaruba, and Manfred Huber. 2012 · 1935
Earlier work this paper cites.
Efficient training of artificial neural networks for autonomous navigation
Dean A Pomerleau. 1991 · 1991
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman. 1994 · 1994
Earlier work this paper cites.
The dynamics of reinforcement learning in cooperative multiagent systems
Caroline Claus and Craig Boutilier. 1998 · 1998
Earlier work this paper cites.
Multiagent reinforcement learning: theoretical framework and an algorithm.. In ICML , Vol. 98. 242–250
Junling Hu, Michael P Wellman, et al · 1998
Earlier work this paper cites.
Coordinated multi-agent imitation learning. In International Conference on Machine Learning . PMLR, 1995–2003
Hoang M Le, Yisong Yue, Peter Carr, and Patrick Lucey. 2017 · 2003
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning . 1
Pieter Abbeel and Andrew Y Ng. 2004 · 2004
Earlier work this paper cites.
Convex optimization
Stephen P Boyd and Lieven Vandenberghe. 2004 · 2004
Earlier work this paper cites.
Optimal and approximate Q-value functions for decentralized POMDPs
Frans A Oliehoek, Matthijs TJ Spaan, and Nikos Vlassis. 2008 · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning.. In Aaai , Vol. 8. Chicago, IL, USA, 1433–1438
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al · 2008
Earlier work this paper cites.
Incorporating Functional Knowledge in Neural Networks
Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, and René Garcia. 2009 · 2009
Earlier work this paper cites.
Efficient reductions for imitation learning. In Proceedings of the thirteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 661–668
Stéphane Ross and Drew Bagnell. 2010 · 2010
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 627–635
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. 2011 · 2011
Earlier work this paper cites.
Multi-robot inverse reinforcement learning under occlusion with interactions. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems . 173–180
Kenneth Bogert and Prashant Doshi. 2014 · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Multi-agent inverse reinforcement learning for zero-sum games
Xiaomin Lin, Peter A Beling, and Randy Cogill. 2014 · 2014
Earlier work this paper cites.
Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine. 2016 · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon. 2016 · 2016
Cited alongside, same era.
Multi-agent reinforcement learning as a rehearsal for decentralized planning
Landon Kraemer and Bikramjit Banerjee. 2016 · 2016
Cited alongside, same era.
A concise introduction to decentralized POMDPs . Vol. 1
Frans A Oliehoek, Christopher Amato, et al · 2016
Cited alongside, same era.
Inverse reinforcement learning in swarm systems
Adrian Šošić, Wasiur R KhudaBukhsh, Abdelhak M Zoubir, and Heinz Koeppl. 2016 · 2016
Cited alongside, same era.
Multi-agent adversarial inverse reinforcement learning. In International Conference on Machine Learning . PMLR, 7194–7201
Lantao Yu, Jiaming Song, and Stefano Ermon. 2019 · 2019
Later among the works it cites.
Is independent learning all you need in the starcraft multi-agent challenge?
Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip HS Torr, Mingfei Sun, and Shimon Whiteson. 2020 · 2020
Later among the works it cites.
FPT Reinforcement Learning Competition
FPT. 2020 · 2020
Later among the works it cites.
Urban driving with conditional imitation learning. In 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 251–257
Jeffrey Hawke, Richard Shen, Corina Gurau, Siddharth Sharma, Daniele Reda, Nikolay Nikolov, Przemysław Mazur, Sean Micklethwaite, Nicolas Griffiths, Amar Shah, et al · 2020
Later among the works it cites.
Scalable and sample-efficient multi-agent imitation learning. In Proceedings of the Workshop on Artificial Intelligence Safety, co-located with 34th AAAI Conference on Artificial Intelligence, SafeAI@ AAAI
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Making friends on the fly: Cooperating with new teammates
Samuel Barrett, Avi Rosenfeld, Sarit Kraus, and Peter Stone. 2017 · 2017
Cited alongside, same era.
Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
HyperNetworks. In International Conference on Learning Representations
David Ha, Andrew M. Dai, and Quoc V. Le. 2017 · 2017
Cited alongside, same era.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. 2017 · 2017
Cited alongside, same era.
Value-decomposition networks for cooperative multi-agent learning
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2017
Cited alongside, same era.
Multi-agent imitation learning for driving simulation. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 1534–1539
Raunak P Bhattacharyya, Derek J Phillips, Blake Wulfe, Jeremy Morton, Alex Kuefler, and Mykel J Kochenderfer. 2018 · 2018
Cited alongside, same era.
Counterfactual multi-agent policy gradients. In Proceedings of the AAAI conference on artificial intelligence , Vol. 32
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. 2018 · 2018
Cited alongside, same era.
Wonseok Jeon, Paul Barde, Derek Nowrouzezahrai, and Joelle Pineau. 2020 · 2020
Later among the works it cites.
Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. 2020 · 2020
Later among the works it cites.
Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes. In Proceedings of The Web Conference 2020 . 1785–1795
Lu Wang, Wenchao Yu, Xiaofeng He, Wei Cheng, Martin Renqiang Ren, Wei Wang, Bo Zong, Haifeng Chen, and Hongyuan Zha. 2020 · 2020
Later among the works it cites.
A survey of inverse reinforcement learning: Challenges, methods and progress
Saurabh Arora and Prashant Doshi. 2021 · 2021
Later among the works it cites.
Iq-learn: Inverse soft-q learning for imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, and Stefano Ermon. 2021 · 2021
Later among the works it cites.
Exploring imitation learning for autonomous driving with feedback synthesizer and differentiable rasterization. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 1450–1457
Jinyun Zhou, Rui Wang, Xu Liu, Yifei Jiang, Shu Jiang, Jiaming Tao, Jinghao Miao, and Shiyu Song. 2021 · 2021
Later among the works it cites.
SMACv2: An improved benchmark for cooperative multi-agent reinforcement learning
Benjamin Ellis, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Mahajan, Jakob N Foerster, and Shimon Whiteson. 2022 · 2022
Later among the works it cites.
A survey on imitation learning techniques for end-to-end autonomous vehicles
Luc Le Mero, Dewei Yi, Mehrdad Dianati, and Alexandros Mouzakitis. 2022 · 2022
Later among the works it cites.
Learning and assessing optimal dynamic treatment regimes through cooperative imitation learning
Syed Ihtesham Hussain Shah, Antonio Coronato, Muddasar Naeem, and Giuseppe De Pietro. 2022 · 2022
Later among the works it cites.
The surprising effectiveness of ppo in cooperative multi-agent games
Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu. 2022 · 2022
Later among the works it cites.
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games
The Viet Bui, Tien Mai, and Thanh Hong Nguyen. 2023 · 2023
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