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Discrete Time Mean Field Game Models
Discrete Time Mean Field Game Models
Traffic Routing game
Traffic Routing game
Main reference: Cabannes, T., Laurière, M., Perolat, J., Marinier, R., Girgin, S., Perrin, S., Pietquin, O., Bayen, A.M., Goubault, E. and Elie, R., 2022, May. Solving N-Player Dynamic Routing Games with Congestion: A Mean-Field Approach. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (pp. 1557-1559).
Crowd Motion
Crowd Motion
Code: https://colab.research.google.com/drive/18c0WaAYNFouV3gIvgp9UtY3NBiiL-Dmp?usp=sharing
Main references:
Fictitious Play: Perrin, S., Pérolat, J., Laurière, M., Geist, M., Elie, R. and Pietquin, O., 2020. Fictitious play for mean field games: Continuous time analysis and applications. Advances in neural information processing systems, 33, pp.13199-13213.
Fictitious Play with Deep RL: Laurière, M., Perrin, S., Girgin, S., Muller, P., Jain, A., Cabannes, T., Piliouras, G., Pérolat, J., Elie, R., Pietquin, O. and Geist, M., 2022, June. Scalable deep reinforcement learning algorithms for mean field games. In International Conference on Machine Learning (pp. 12078-12095). PMLR.
Continuous Time Mean Field Game Models
Continuous Time Mean Field Game Models
Epidemics Control
Epidemics Control
Code: https://github.com/gokce-d/StackelbergMFG
Main reference: Aurell, A., Carmona, R., Dayanikli, G. and Lauriere, M., 2022. Optimal incentives to mitigate epidemics: a Stackelberg mean field game approach. SIAM Journal on Control and Optimization, 60(2), pp.S294-S322.
Optimal Execution
Optimal Execution
Code: [Coming soon!]
Main references:
Model: Cardaliaguet, P. and Lehalle, C.A., 2018. Mean field game of controls and an application to trade crowding. Mathematics and Financial Economics, 12, pp.335-363.
Deep learning: Carmona, R. and Laurière, M., 2023. Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance. Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices, p.369.
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