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Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard challenge.
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Carmona, R., Laurière, M., and Tan, Z. (2019) · 1910
Earlier work this paper cites.
Approximately solving mean field games via entropy-regularized deep reinforcement learning
Cui, K. and Koeppl, H. (2021) · 1917
Earlier work this paper cites.
Inductive reasoning and bounded rationality
Arthur, W. B. (1994) · 1994
Earlier work this paper cites.
Emergence of scaling in random networks
Barabási, A.-L. and Albert, R. (1999) · 1999
Earlier work this paper cites.
Mean-field theory for scale-free random networks
Barabási, A.-L., Albert, R., and Jeong, H. (1999) · 1999
Earlier work this paper cites.
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Elie, R., Ichiba, T., and Laurière, M. (2020a) · 2001
Earlier work this paper cites.
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Papadimitriou, C. (2001) · 2001
Earlier work this paper cites.
Fair and efficient solutions to the santa fe bar problem
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Power law distributions in class relationships
Wheeldon, R. and Counsell, S. (2003) · 2003
Earlier work this paper cites.
Nash equilibria of games with a continuum of players
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Borgs, C., Chayes, J., Lovász, L., Sós, V., and Vesztergombi, K. (2011) · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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