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We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash Equilibrium policy.
Large population stochastic dynamic games: closed-loop mckean-vlasov systems and the nash certainty equivalence principle
Huang, M., Malhamé, R. P., and Caines, P. E · 2006
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Near-optimal regret bounds for reinforcement learning
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Eluder dimension and the sample complexity of optimistic exploration
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Model-based reinforcement learning and the eluder dimension
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Mean-field-type games in engineering
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Minimax regret bounds for reinforcement learning
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Contextual decision processes with low bellman rank are pac-learnable
Jiang, N., Krishnamurthy, A., Agarwal, A., Langford, J., and Schapire, R. E · 2017
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Mean field game of controls and an application to trade crowding
Cardaliaguet, P. and Lehalle, C.-A · 2018
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Is q-learning provably efficient?
Jin, C., Allen-Zhu, Z., Bubeck, S., and Jordan, M. I · 2018
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Linear quadratic mean field stackelberg differential games
Moon, J. and Başar, T · 2018
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Learning mean-field games
Guo, X., Hu, A., Xu, R., and Zhang, J · 2019
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Reinforcement learning in stationary mean-field games
Subramanian, J. and Mahajan, A · 2019
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Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches
Sun, W., Jiang, N., Krishnamurthy, A., Agarwal, A., and Langford, J · 2019
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Policy optimization provably converges to nash equilibria in zero-sum linear quadratic games
Zhang, K., Yang, Z., and Basar, T · 2019
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Flambe: Structural complexity and representation learning of low rank mdps
Agarwal, A., Kakade, S., Krishnamurthy, A., and Sun, W · 2020
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Model-based reinforcement learning with value-targeted regression
Ayoub, A., Jia, Z., Szepesvari, C., Wang, M., and Yang, L · 2020
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Near-optimal reinforcement learning with self-play
Bai, Y., Jin, C., and Yu, T · 2020
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On the convergence of model free learning in mean field games
Elie, R., Perolat, J., Laurière, M., Geist, M., and Pietquin, O · 2020
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Ghosh, A. and Aggarwal, V · 2020
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Provably efficient reinforcement learning with linear function approximation
Jin, C., Yang, Z., Wang, Z., and Jordan, M. I · 2020
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Model-free reinforcement learning for non-stationary mean field games
Mishra, R. K., Vasal, D., and Vishwanath, S · 2020
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Fictitious play for mean field games: Continuous time analysis and applications
Perrin, S., Pérolat, J., Laurière, M., Geist, M., Elie, R., and Pietquin, O · 2020
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Learning mean field games: A survey
Laurière, M., Perrin, S., Geist, M., and Pietquin, O · 2022
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Eluder-based regret for stochastic contextual mdps, 2022
Levy, O., Cassel, A., Cohen, A., and Mansour, Y · 2022
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A mean-field game approach to cloud resource management with function approximation
Mao, W., Qiu, H., Wang, C., Franke, H., Kalbarczyk, Z., Iyer, R., and Basar, T · 2022
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Representation learning for general-sum low-rank markov games
Ni, C., Song, Y., Zhang, X., Jin, C., and Wang, M · 2022
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Master equation for discrete-time stackelberg mean field games with a single leader
Vasal, D. and Berry, R · 2022
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Subramanian, S. G., Poupart, P., Taylor, M. E., and Hegde, N · 2020
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Learning near optimal policies with low inherent bellman error
Zanette, A., Lazaric, A., Kochenderfer, M., and Brunskill, E · 2020
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Approximately solving mean field games via entropy-regularized deep reinforcement learning
Cui, K. and Koeppl, H · 2021
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Bilinear classes: A structural framework for provable generalization in rl
Du, S., Kakade, S., Lee, J., Lovett, S., Mahajan, G., Sun, W., and Wang, R · 2021
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The statistical complexity of interactive decision making
Foster, D. J., Kakade, S. M., Qian, J., and Rakhlin, A · 2021
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Towards general function approximation in zero-sum markov games
Huang, B., Lee, J. D., Wang, Z., and Yang, Z · 2021
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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 · 2021
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Xie, T., Foster, D. J., Bai, Y., Jiang, N., and Kakade, S. M · 2022
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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 · 2022
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A posterior sampling framework for interactive decision making
Zhong, H., Xiong, W., Zheng, S., Wang, L., Wang, Z., Yang, Z., and Zhang, T · 2022
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Q-learning in regularized mean-field games
Anahtarci, B., Kariksiz, C. D., and Saldi, N · 2023
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Cui, Q., Zhang, K., and Du, S. S · 2023
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The complexity of markov equilibrium in stochastic games
Daskalakis, C., Golowich, N., and Zhang, K · 2023
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On the complexity of multi-agent decision making: From learning in games to partial monitoring
Foster, D., Foster, D. J., Golowich, N., and Rakhlin, A · 2023
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Reinforcement learning for non-stationary discrete-time linear–quadratic mean-field games in multiple populations
uz Zaman, M. A., Miehling, E., and Başar, T · 2023
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Wang, Y., Liu, Q., Bai, Y., and Jin, C · 2023
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Learning regularized monotone graphon mean-field games
Zhang, F., Tan, V. Y., Wang, Z., and Yang, Z · 2023
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On the statistical efficiency of mean-field reinforcement learning with general function approximation
Huang, J., Yardim, B., and He, N · 2024
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When is mean-field reinforcement learning tractable and relevant?
Yardim, B., Goldman, A., and He, N · 2024
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