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We investigate reinforcement learning in the setting of Markov decision processes for a large number of exchangeable agents interacting in a mean field manner.
A topological property of real analytic subsets
Lojasiewicz, S. (1963) · 1963
Earlier work this paper cites.
Gradient methods for minimizing functionals
Polyak, B. T. (1963) · 1963
Earlier work this paper cites.
Evolutionsstrategie: Optimierung technischer systeme nach prinzipien der biologischen evolution, frommann–holzboog
Rechenberg, I. (1973) · 1973
Earlier work this paper cites.
Existence and comparison theorems for algebraic riccati equations for continuous-and discrete-time systems
Ran, A. and Vreugdenhil, R. (1988) · 1988
Earlier work this paper cites.
Online convex optimization in the bandit setting: gradient descent without a gradient
Flaxman, A. D., Kalai, A. T., and McMahan, H. B. (2005) · 2005
Earlier work this paper cites.
Introduction to derivative-free optimization
Conn, A. R., Scheinberg, K., and Vicente, L. N. (2009) · 2009
Earlier work this paper cites.
Mean field for markov decision processes: from discrete to continuous optimization
Gast, N., Gaujal, B., and Le Boudec, J.-Y. (2012) · 2012
Earlier work this paper cites.
User-friendly tail bounds for sums of random matrices
Tropp, J. A. (2012) · 2012
Earlier work this paper cites.
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Bensoussan, A., Frehse, J., and Yam, S. C. P. (2015) · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Karimi, H., Nutini, J., and Schmidt, M. (2016) · 2016
Earlier work this paper cites.
Dynamic programming for mean-field type control
Laurière, M. and Pironneau, O. (2016) · 2016
Earlier work this paper cites.
Random gradient-free minimization of convex functions
Nesterov, Y. and Spokoiny, V. (2017) · 2017
Earlier work this paper cites.
Dynamic programming for optimal control of stochastic McKean-Vlasov dynamics
Pham, H. and Wei, X. (2017) · 2017
Earlier work this paper cites.
Evolution strategies as a scalable alternative to reinforcement learning
Salimans, T., Ho, J., Chen, X., Sidor, S., and Sutskever, I. (2017) · 2017
Earlier work this paper cites.
Lyapunov theory for discrete time systems
Bof, N., Carli, R., and Schenato, L. (2018) · 2018
Earlier work this paper cites.
Probabilistic theory of mean field games with applications. I
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Earlier work this paper cites.
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Yang, Y., Luo, R., Li, M., Zhou, M., Zhang, W., and Wang, J. (2018) · 2018
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Actor-critic provably finds nash equilibria of linear-quadratic mean-field games
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Mean-field markov decision processes with common noise and open-loop controls
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