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This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training.
Games with incomplete information played by “bayesian” players, i–iii part i. the basic model
Harsanyi, J. C · 1967
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Feudal reinforcement learning
Dayan, P. and Hinton, G. E · 1993
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Markov games as a framework for multi-agent reinforcement learning
Littman, M. L · 1994
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Evolutionary game dynamics
Hofbauer, J. and Sigmund, K · 2003
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Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Sutton, R. S., Modayil, J., Delp, M., Degris, T., Pilarski, P. M., White, A., and Precup, D · 2011
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Solving imperfect-information games
Sandholm, T · 2015
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Reinforcement learning with unsupervised auxiliary tasks
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., and Kavukcuoglu, K · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Hindsight experience replay
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, O. P., and Zaremba, W · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Pérolat, J., Silver, D., and Graepel, T · 2017
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Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., Orseau, L., and Legg, S · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, O. P., and Mordatch, I · 2017
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Imagination-augmented agents for deep reinforcement learning
Racanière, S., Weber, T., Reichert, D., Buesing, L., Guez, A., Rezende, D. J., Badia, A. P., Vinyals, O., Heess, N., Li, Y., et al · 2017
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Rauber, P., Ummadisingu, A., Mutz, F., and Schmidhuber, J · 2017
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Feudal networks for hierarchical reinforcement learning
Vezhnevets, A. S., Osindero, S., Schaul, T., Heess, N., Jaderberg, M., Silver, D., and Kavukcuoglu, K · 2017
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Re-evaluating evaluation
Balduzzi, D., Tuyls, K., Perolat, J., and Graepel, T · 2018
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Hierarchical deep multiagent reinforcement learning
Tang, H., Hao, J., Lv, T., Chen, Y., Zhang, Z., Jia, H., Ren, C., Zheng, Y., Fan, C., and Wang, L · 2018
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Feudal multi-agent hierarchies for cooperative reinforcement learning
Ahilan, S. and Dayan, P · 2019
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Emergent tool use from multi-agent autocurricula
Baker, B., Kanitscheider, I., Markov, T., Wu, Y., Powell, G., McGrew, B., and Mordatch, I · 2019
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Open-ended learning in symmetric zero-sum games
Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W. M., Perolat, J., Jaderberg, M., and Graepel, T · 2019
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Woulda, coulda, shoulda: Counterfactually-guided policy search
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Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., and Kavukcuoglu, K · 2018
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Counterfactual multi-agent policy gradients
Foerster, J. N., Farquhar, G., Afouras, T., Nardelli, N., and Whiteson, S · 2018
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Inequity aversion improves cooperation in intertemporal social dilemmas
Hughes, E., Leibo, J. Z., Phillips, M., Tuyls, K., Dueñez-Guzman, E., Castañeda, A. G., Dunning, I., Zhu, T., McKee, K., Koster, R., et al · 2018
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Actor-attention-critic for multi-agent reinforcement learning
Iqbal, S. and Sha, F · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Machado, M. C., Bellemare, M. G., Talvitie, E., Veness, J., Hausknecht, M., and Bowling, M · 2018
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Qmix: monotonic value function factorisation for deep multi-agent reinforcement learning
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Buesing, L., Weber, T., Zwols, Y., Heess, N., Racaniere, S., Guez, A., and Lespiau, J.-B · 2019
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Quantifying generalization in reinforcement learning
Cobbe, K., Klimov, O., Hesse, C., Kim, T., and Schulman, J · 2019
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Hindsight credit assignment
Harutyunyan, A., Dabney, W., Mesnard, T., Gheshlaghi Azar, M., Piot, B., Heess, N., van Hasselt, H. P., Wayne, G., Singh, S., Precup, D., and Munos, R · 2019
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Leibo, J. Z., Hughes, E., Lanctot, M., and Graepel, T · 2019
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{ \ a l p h a } \{\backslash alpha\} -rank: Multi-agent evaluation by evolution
Omidshafiei, S., Papadimitriou, C., Piliouras, G., Tuyls, K., Rowland, M., Lespiau, J.-B., Czarnecki, W. M., Lanctot, M., Perolat, J., and Munos, R · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al · 2019
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