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In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research.
Multi-agent reinforcement learning: Independent vs. cooperative agents
Tan, M · 1993
Earlier work this paper cites.
Robocup: The robot world cup initiative
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., and Osawa, E · 1997
Earlier work this paper cites.
Keepaway soccer: From machine learning testbed to benchmark
Stone, P., Kuhlmann, G., Taylor, M. E., and Liu, Y · 2005
Earlier work this paper cites.
Half field offense in robocup soccer: A multiagent reinforcement learning case study
Kalyanakrishnan, S., Liu, Y., and Stone, P · 2006
Earlier work this paper cites.
Optimal and Approximate Q-value Functions for Decentralized POMDPs
Oliehoek, F. A., Spaan, M. T. J., and Nikos Vlassis · 2008
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
Earlier work this paper cites.
A survey of real-time strategy game ai research and competition in starcraft
Ontanón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., and Preuss, M · 2013
Earlier work this paper cites.
Deep Recurrent Q-Learning for Partially Observable MDPs
Hausknecht, M. and Stone, P · 2015
Earlier work this paper cites.
Multiagent Cooperation and Competition with Deep Reinforcement Learning
Tampuu, A., Matiisen, T., Kodelja, D., Kuzovkin, I., Korjus, K., Aru, J., Aru, J., and Vicente, R · 2015
Earlier work this paper cites.
Half field offense: an environment for multiagent learning and ad hoc teamwork
Hausknecht, M., Mupparaju, P., Subramanian, S., Kalyanakrishnan, S., and Stone, P · 2016
Earlier work this paper cites.
Deep reinforcement learning from self-play in imperfect-information games
Heinrich, J. and Silver, D · 2016
Earlier work this paper cites.
Multi-agent reinforcement learning as a rehearsal for decentralized planning
Kraemer, L. and Banerjee, B · 2016
Cited alongside, same era.
A Concise Introduction to Decentralized POMDPs
Oliehoek, F. A. and Amato, C · 2016
Cited alongside, same era.
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Synnaeve, G., Nardelli, N., Auvolat, A., Chintala, S., Lacroix, T., Lin, Z., Richoux, F., and Usunier, N · 2016
Cited alongside, same era.
Usunier, N., Synnaeve, G., Lin, Z., and Chintala, S · 2016
Cited alongside, same era.
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Foerster, J., Nardelli, N., Farquhar, G., Afouras, T., Torr, P. H. S., Kohli, P., and Whiteson, S · 2017
Cited alongside, same era.
Nardelli, N., Synnaeve, G., Lin, Z., Kohli, P., Torr, P. H., and Usunier, N · 2018
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On reinforcement learning for full-length game of starcraft
Pang, Z.-J., Liu, R.-Z., Meng, Z.-Y., Zhang, Y., Yu, Y., and Lu, T · 2018
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Multi-goal reinforcement learning: Challenging robotics environments and request for research, 2018
Plappert, M., Andrychowicz, M., Ray, A., McGrew, B., Baker, B., Powell, G., Schneider, J., Tobin, J., Chociej, M., Welinder, P., Kumar, V., and Zaremba, W · 2018
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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Rashid, T., Samvelyan, M., Schroeder, C., Farquhar, G., Foerster, J., and Whiteson, S · 2018
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Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., and Graepel, T · 2017
Cited alongside, same era.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., and Mordatch, I · 2017
Cited alongside, same era.
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., and Graepel, T · 2017
Cited alongside, same era.
StarCraft II: A New Challenge for Reinforcement Learning
Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., Küttler, H., Agapiou, J., Schrittwieser, J., Quan, J., Gaffney, S., Petersen, S., Simonyan, K., Schaul, T., van Hasselt, H., Silver, D., Lillicrap, T., Calderone, K., Keet, P., Brunasso, A., Lawrence, D., Ekermo, A., Repp, J., and Tsing, R · 2017
Cited alongside, same era.
Magent: A many-agent reinforcement learning platform for artificial collective intelligence
Zheng, L., Yang, J., Cai, H., Zhang, W., Wang, J., and Yu, Y · 2017
Cited alongside, same era.
Knowledge-guided agent-tactic-aware learning for starcraft micromanagement
Hu, Y., Li, J., Li, X., Pan, G., and Xu, M · 2018
Cited alongside, same era.
Counterfactual multi-agent policy gradients
Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., and Whiteson, S
Cited in the paper.
Resnick, C., Eldridge, W., Ha, D., Britz, D., Foerster, J., Togelius, J., Cho, K., and Bruna, J · 2018
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Starcraft micromanagement with reinforcement learning and curriculum transfer learning
Shao, K., Zhu, Y., and Zhao, D · 2018
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Tstarbots: Defeating the cheating level builtin ai in starcraft ii in the full game
Sun, P., Sun, X., Han, L., Xiong, J., Wang, Q., Li, B., Zheng, Y., Liu, J., Liu, Y., Liu, H., et al · 2018
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New blizzard custom game: Starcraft master
Blizzard Entertainment · 2018
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Mean field multi-agent reinforcement learning
Yang, Y., Luo, R., Li, M., Zhou, M., Zhang, W., and Wang, J · 2018
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Alphastar: Mastering the real-time strategy game starcraft ii, 2019
DeepMind · 2019
Closest in time.
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
Closest in time.