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In this paper, we study the fundamental statistical efficiency of Reinforcement Learning in Mean-Field Control (MFC) and Mean-Field Game (MFG) with general model-based function approximation.
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Policy optimization provably converges to nash equilibria in zero-sum linear quadratic games
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Ayoub, A., Jia, Z., Szepesvari, C., Wang, M., and Yang, L. (2020) · 2020
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Unified reinforcement q-learning for mean field game and control problems
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Concave utility reinforcement learning: The mean-field game viewpoint
Geist, M., Pérolat, J., Laurière, M., Elie, R., Perrin, S., Bachem, O., Munos, R., and Pietquin, O. (2022) · 2022
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Entropy regularization for mean field games with learning
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Provably efficient reinforcement learning with linear function approximation
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Sample complexity of reinforcement learning using linearly combined model ensembles
Modi, A., Jiang, N., Tewari, A., and Singh, S. (2020) · 2020
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Huang, J., Chen, J., Zhao, L., Qin, T., Jiang, N., and Liu, T.-Y. (2022) · 2022
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Eluder-based regret for stochastic contextual mdps
Levy, O., Cassel, A., Cohen, A., and Mansour, Y. (2022) · 2022
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Scaling mean field games by online mirror descent
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The role of coverage in online reinforcement learning
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A self-play posterior sampling algorithm for zero-sum markov games
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Q-learning in regularized mean-field games
Anahtarci, B., Kariksiz, C. D., and Saldi, N. (2023) · 2023
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Representation learning for low-rank general-sum markov games
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Efficient model-based multi-agent mean-field reinforcement learning
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Policy mirror ascent for efficient and independent learning in mean field games
Yardim, B., Cayci, S., Geist, M., and He, N. (2023) · 2023
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Model-free representation learning and exploration in low-rank mdps
Modi, A., Chen, J., Krishnamurthy, A., Jiang, N., and Agarwal, A. (2024) · 2024
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