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[1] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[2] Pablo Hernandez-Leal,et al. A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity , 2017, ArXiv.
[3] Andreas Krause,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.
[4] Peter Auer,et al. Near-optimal Regret Bounds for Reinforcement Learning , 2008, J. Mach. Learn. Res..
[5] Michal Valko,et al. Regret Bounds for Kernel-Based Reinforcement Learning , 2020, ArXiv.
[6] Sean P. Meyn,et al. Learning in Mean-Field Games , 2014, IEEE Transactions on Automatic Control.
[7] Yongxin Chen,et al. Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games , 2019, ICLR.
[8] Aditya Gopalan,et al. Online Learning in Kernelized Markov Decision Processes , 2019, AISTATS.
[9] Saeid Nahavandi,et al. Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications , 2018, IEEE Transactions on Cybernetics.
[10] Olivier Guéant,et al. Mean Field Games and Applications , 2011 .
[11] Ronen I. Brafman,et al. A near-optimal polynomial time algorithm for learning in certain classes of stochastic games , 2000, Artif. Intell..
[12] René Carmona,et al. Probabilistic Analysis of Mean-field Games , 2013 .
[13] Wojciech M. Czarnecki,et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning , 2019, Nature.
[14] Matthew E. Taylor,et al. A survey and critique of multiagent deep reinforcement learning , 2019, Autonomous Agents and Multi-Agent Systems.
[15] Felix Berkenkamp,et al. Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning , 2020, NeurIPS.
[16] Yoav Zemel,et al. Statistical Aspects of Wasserstein Distances , 2018, Annual Review of Statistics and Its Application.
[17] Joelle Pineau,et al. Streaming kernel regression with provably adaptive mean, variance, and regularization , 2017, J. Mach. Learn. Res..
[18] S. Kakade,et al. Information Theoretic Regret Bounds for Online Nonlinear Control , 2020, NeurIPS.
[19] Bart De Schutter,et al. A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[20] Michael I. Jordan,et al. Provably Efficient Reinforcement Learning with Linear Function Approximation , 2019, COLT.
[21] Sergey Levine,et al. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.
[22] Andreas Krause,et al. Contextual Gaussian Process Bandit Optimization , 2011, NIPS.
[23] Renyuan Xu,et al. Dynamic Programming Principles for Mean-Field Controls with Learning , 2019, Oper. Res..
[24] Shie Mannor,et al. Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies , 2019, NeurIPS.
[25] Ming Zhou,et al. Mean Field Multi-Agent Reinforcement Learning , 2018, ICML.
[26] Renyuan Xu,et al. Q-Learning Algorithm for Mean-Field Controls, with Convergence and Complexity Analysis , 2020 .
[27] Hongyuan Zha,et al. Learning Deep Mean Field Games for Modeling Large Population Behavior , 2017, ICLR.
[28] Byoung-Tak Zhang,et al. Stock Trading System Using Reinforcement Learning with Cooperative Agents , 2002, ICML.
[29] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[30] Stefano Ermon,et al. Accurate Uncertainties for Deep Learning Using Calibrated Regression , 2018, ICML.
[31] P. Lions,et al. Jeux à champ moyen. I – Le cas stationnaire , 2006 .
[32] Jonghun Park,et al. A Multiagent Approach to $Q$-Learning for Daily Stock Trading , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[33] Amnon Shashua,et al. Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving , 2016, ArXiv.
[34] Sean P. Meyn,et al. Learning in mean-field oscillator games , 2010, 49th IEEE Conference on Decision and Control (CDC).
[35] Baher Abdulhai,et al. Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto , 2013, IEEE Transactions on Intelligent Transportation Systems.
[36] Sergey Levine,et al. Optimism-driven exploration for nonlinear systems , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[37] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[38] On the Convergence of Model Free Learning in Mean Field Games , 2019, AAAI.
[39] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[40] Peter E. Caines,et al. Large population stochastic dynamic games: closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle , 2006, Commun. Inf. Syst..
[41] Cristiano Castelfranchi,et al. The theory of social functions: challenges for computational social science and multi-agent learning , 2001, Cognitive Systems Research.
[42] Charafeddine Mouzouni,et al. A Mean Field Game Of Portfolio Trading And Its Consequences On Perceived Correlations , 2019, 1902.09606.
[43] J. Fouque,et al. Unified reinforcement Q-learning for mean field game and control problems , 2020, Mathematics of Control, Signals, and Systems.
[44] P. Lions,et al. Jeux à champ moyen. II – Horizon fini et contrôle optimal , 2006 .
[45] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[46] Vaneet Aggarwal,et al. Reinforcement Learning for Mean Field Game , 2019, Algorithms.
[47] Diogo Gomes,et al. Two Numerical Approaches to Stationary Mean-Field Games , 2015, Dyn. Games Appl..
[48] M'ed'eric Motte UPD7,et al. Mean-field Markov decision processes with common noise and open-loop controls , 2019, The Annals of Applied Probability.
[49] R. Carmona,et al. Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning , 2019, The Annals of Applied Probability.
[50] Alexis Boukouvalas,et al. GPflow: A Gaussian Process Library using TensorFlow , 2016, J. Mach. Learn. Res..
[51] Aditya Mahajan,et al. Reinforcement Learning in Stationary Mean-field Games , 2019, AAMAS.
[52] Csaba Szepesvári,et al. Regret Bounds for the Adaptive Control of Linear Quadratic Systems , 2011, COLT.
[53] Ronen I. Brafman,et al. R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning , 2001, J. Mach. Learn. Res..
[54] Pierre Cardaliaguet,et al. Learning in mean field games: The fictitious play , 2015, 1507.06280.
[55] Joel Z. Leibo,et al. Multi-agent Reinforcement Learning in Sequential Social Dilemmas , 2017, AAMAS.
[56] Diogo A. Gomes,et al. Mean Field Games Models—A Brief Survey , 2013, Dynamic Games and Applications.
[57] Daniel Lacker,et al. Limit Theory for Controlled McKean-Vlasov Dynamics , 2016, SIAM J. Control. Optim..
[58] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[59] Renyuan Xu,et al. A General Framework for Learning Mean-Field Games , 2020, Mathematics of Operations Research.
[60] Afshin Oroojlooyjadid,et al. A review of cooperative multi-agent deep reinforcement learning , 2019, Applied Intelligence.
[61] Zhuoran Yang,et al. Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning , 2020, ICML.
[62] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .