Actor-Critic Reinforcement Learning Algorithms for Mean Field Games in Continuous Time, State and Action Spaces

Author:

Liang Hong,Chen ZhipingORCID,Jing Kaili

Funder

the National Key R &D Program of China

Publisher

Springer Science and Business Media LLC

Reference57 articles.

1. Adel, C.: The relaxed optimal control problem for mean-field sdes systems and application. Automatica 50(3), 924–930 (2014)

2. Anahtarci, B., Kariksiz, C.D., Saldi, N.: Q-learning in regularized mean-field games. Dyn. Games Appl. 13(1), 89–117 (2023)

3. Anahtarci, B., Kariksiz, C.D., Saldi, N.: Fitted q-learning in mean-field games. arXiv preprint http://arxiv.org/abs/1912.13309 (2019)

4. Angiuli, A., Fouque, J.P., Hu, R., Raydan, A.: Deep reinforcement learning for infinite horizon mean field problems in continuous spaces. http://arxiv.org/abs/2309.10953 (2023)

5. Angiuli, A., Fouque, J.P., Laurière, M.: Unified reinforcement q-learning for mean field game and control problems. Math. Control Signals Syst. 34(2), 217–271 (2022)

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