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Training agents in multi-agent competitive games presents significant challenges due to their intricate nature.
The starcraft multi-agent challenge
Samvelyan, M.; Rashid, T.; De Witt, C. S.; Farquhar, G.; Nardelli, N.; Rudner, T. G.; Hung, C.-M.; Torr, P. H.; Foerster, J.; and Whiteson, S. 2019 · 1902
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Sqil: Imitation learning via reinforcement learning with sparse rewards
Reddy, S.; Dragan, A. D.; and Levine, S. 2019 · 1905
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Markov games as a framework for multi-agent reinforcement learning
Littman, M. L. 1994 · 1994
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The dynamics of reinforcement learning in cooperative multiagent systems
Claus, C.; and Boutilier, C. 1998 · 1998
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Algorithms for inverse reinforcement learning
Ng, A. Y.; Russell, S.; et al. 2000 · 2000
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Apprenticeship learning via inverse reinforcement learning
Abbeel, P.; and Ng, A. Y. 2004 · 2004
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Is independent learning all you need in the starcraft multi-agent challenge?
de Witt, C. S.; Gupta, T.; Makoviichuk, D.; Makoviychuk, V.; Torr, P. H.; Sun, M.; and Whiteson, S. 2020 · 2011
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Generative adversarial imitation learning
Ho, J.; and Ermon, S. 2016 · 2016
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Learning robust rewards with adversarial inverse reinforcement learning
Fu, J.; Luo, K.; and Levine, S. 2017 · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R.; Wu, Y. I.; Tamar, A.; Harb, J.; Pieter Abbeel, O.; and Mordatch, I. 2017 · 2017
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Proximal policy optimization algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
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Value-decomposition networks for cooperative multi-agent learning
Sunehag, P.; Lever, G.; Gruslys, A.; Czarnecki, W. M.; Zambaldi, V.; Jaderberg, M.; Lanctot, M.; Sonnerat, N.; Leibo, J. Z.; Tuyls, K.; et al. 2017 · 2017
FPT Reinforcement Learning Competition
FPT. 2020 · 2020
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Google research football: A novel reinforcement learning environment
Kurach, K.; Raichuk, A.; Stańczyk, P.; Zając, M.; Bachem, O.; Espeholt, L.; Riquelme, C.; Vincent, D.; Michalski, M.; Bousquet, O.; et al. 2020 · 2020
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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. 2020 · 2020
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Iq-learn: Inverse soft-q learning for imitation
Garg, D.; Chakraborty, S.; Cundy, C.; Song, J.; and Ermon, S. 2021 · 2021
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SMACv2: An improved benchmark for cooperative multi-agent reinforcement learning
Ellis, B.; Moalla, S.; Samvelyan, M.; Sun, M.; Mahajan, A.; Foerster, J. N.; and Whiteson, S. 2022 · 2022
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Cited alongside, same era.
Counterfactual multi-agent policy gradients
Foerster, J.; Farquhar, G.; Afouras, T.; Nardelli, N.; and Whiteson, S. 2018 · 2018
Cited alongside, same era.
Multi-agent generative adversarial imitation learning
Song, J.; Ren, H.; Sadigh, D.; and Ermon, S. 2018 · 2018
Cited alongside, same era.
The surprising effectiveness of ppo in cooperative multi-agent games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2022 · 2022
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