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Methods like multi-agent reinforcement learning struggle to scale with growing population size.
Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching
Lin, L.-J · 1992
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Optimal dynamic information provision in traffic routing
Meigs, E., Parise, F., Ozdaglar, A. E., and Acemoglu, D · 2001
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Coordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., and Morse, A · 2003
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The Complexity of Computing a Nash Equilibrium
Daskalakis, C., Goldberg, P. W., and Papadimitriou, C. H · 2006
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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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A Comprehensive Survey of Multiagent Reinforcement Learning
Busoniu, L., Babuska, R., and De Schutter, B · 2007
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Mean Field Games
Lasry, J.-M. and Lions, P.-L · 2007
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Max-consensus in a max-plus algebraic setting: The case of fixed communication topologies
Nejad, B. M., Attia, S. A., and Raisch, J · 2009
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Mean Field Games: Numerical Methods
Achdou, Y. and Capuzzo-Dolcetta, I · 2010
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Distributed Averaging in Dynamic Networks
Rajagopalan, S. and Shah, D · 2010
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Tembine, H., Tempone, R., and Vilanova, P · 2012
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Generic Behaviour Similarity Measures for Evolutionary Swarm Robotics
Gomes, J. and Christensen, A. L · 2013
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A Fully Discrete Semi-Lagrangian Scheme for a First Order Mean Field Game Problem
Carlini, E. and Silva, F. J · 2014
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Combining environment-driven adaptation and task-driven optimisation in evolutionary robotics
Haasdijk, E., Bredeche, N., and Eiben, A. E · 2014
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Crowd-Averse Cyber-Physical Systems: The Paradigm of Robust Mean-Field Games
Bauso, D. and Tembine, H · 2015
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What Is an Evolutionary Algorithm? , pp. 25–48
Eiben, A. E. and Smith, J. E · 2015
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A Mean Field control approach for demand side management of large populations of Thermostatically Controlled Loads
Grammatico, S., Gentile, B., Parise, F., and Lygeros, J · 2015
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Constrained linear quadratic deterministic mean field control: Decentralized convergence to Nash equilibria in large populations of heterogeneous agents
Grammatico, S., Parise, F., and Lygeros, J · 2015
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Decentralized Convergence to Nash Equilibria in Constrained Deterministic Mean Field Control
Grammatico, S., Parise, F., Colombino, M., and Lygeros, J · 2015
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Improving Survivability in Environment-Driven Distributed Evolutionary Algorithms through Explicit Relative Fitness and Fitness Proportionate Communication
Hart, E., Steyven, A., and Paechter, B · 2015
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Network Aggregative Games and Distributed Mean Field Control via Consensus Theory
Parise, F., Grammatico, S., Gentile, B., and Lygeros, J · 2015
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Embodied Evolution for Collective Indoor Surveillance and Location
Trueba, P., Prieto, A., Bellas, F., and Duro, R. J · 2015
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Emergent structures assembled by large swarms of simple robots
Andréen, D., Jenning, P., Napp, N., and Petersen, K · 2016
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Robust mean field games
Bauso, D., Tembine, H., and Başar, T · 2016
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An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control , pp. 47–66
Mannion, P., Duggan, J., and Howley, E · 2016
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Linear Quadratic Risk-Sensitive and Robust Mean Field Games
Moon, J. and Başar, T · 2016
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Real-time optimization of dynamic problems through distributed embodied evolution
Prieto, A., Bellas, F., Trueba, P., and Duro, R. J · 2016
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Markov game approach for multi-agent competitive bidding strategies in electricity market
Rashedi, N., Tajeddini, M. A., and Kebriaei, H · 2016
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Safe, multi-agent, reinforcement learning for autonomous driving
Shalev-Shwartz, S., Shammah, S., and Shashua, A · 2016
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Learning in mean field games: The fictitious play
Cardaliaguet, Pierre and Hadikhanloo, Saeed · 2017
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Robust Mean Field Linear-Quadratic-Gaussian Games with Unknown L 2 L^{2} -Disturbance
Huang, J. and Huang, M · 2017
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Multi-Agent Reinforcement Learning in Sequential Social Dilemmas
Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., and Graepel, T · 2017
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Decentralized Reinforcement Learning of Robot Behaviors
Leottau, D. L., del Solar, J. R., and Babuka, R · 2017
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A deeper look at experience replay
Zhang, S. and Sutton, R. S · 2017
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Proximal methods for stationary Mean Field Games with local couplings
Briceño-Arias, L., Kalise, D., and Silva, F · 2018
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Embodied evolution of self-organised aggregation by cultural propagation
Cambier, N., Frémont, V., Trianni, V., and Ferrante, E · 2018
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Emergent Communication through Negotiation
Cao, K., Lazaridou, A., Lanctot, M., Leibo, J. Z., Tuyls, K., and Clark, S · 2018
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Maintaining Diversity in Robot Swarms with Distributed Embodied Evolution
Fernández Pérez, I., Boumaza, A., and Charpillet, F · 2018
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Decentralised Learning in Systems With Many, Many Strategic Agents
Mguni, D., Jennings, J., and Munoz de Cote, E · 2018
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Markov–Nash Equilibria in Mean-Field Games with Discounted Cost
Saldi, N., Başar, T., and Raginsky, M · 2018
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Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G · 2018
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Mean field limit of a behavioral financial market model
Trimborn, T., Frank, M., and Martin, S · 2018
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Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Wai, H.-T., Yang, Z., Wang, Z., and Hong, M · 2018
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Distributed Interference-Aware Power Control in Ultra-Dense Small Cell Networks: A Robust Mean Field Game
Yang, C., Dai, H., Li, J., Zhang, Y., and Han, Z · 2018
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Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Zhang, K., Yang, Z., Liu, H., Zhang, T., and Basar, T · 2018
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MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Zheng, L., Yang, J., Cai, H., Zhou, M., Zhang, W., Wang, J., and Yu, Y · 2018
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Fitted Q-Learning in Mean-field Games
Anahtarci, B., Karıksız, C. D., and Saldi, N · 2019
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al · 2019
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Influence of Local Selection and Robot Swarm Density on the Distributed Evolution of GRNs
Fernández Pérez, I. and Sanchez, S · 2019
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Actor-critic provably finds Nash equilibria of linear-quadratic mean-field games
Fu, Z., Yang, Z., Chen, Y., and Wang, Z · 2019
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Learning Mean-Field Games
Guo, X., Hu, A., Xu, R., and Zhang, J · 2019
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Learning Mean-Field Games
Guo, X., Hu, A., Xu, R., and Zhang, J · 2019
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
Jaques, N., Lazaridou, A., Hughes, E., Gulcehre, C., Ortega, P., Strouse, D., Leibo, J. Z., and De Freitas, N · 2019
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The StarCraft Multi-Agent Challenge
Samvelyan, M., Rashid, T., Schroeder de Witt, C., Farquhar, G., Nardelli, N., Rudner, T. G. J., Hung, C.-M., Torr, P. H. S., Foerster, J., and Whiteson, S · 2019
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Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach
Shiri, H., Park, J., and Bennis, M · 2019
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Reinforcement Learning in Stationary Mean-Field Games
Subramanian, J. and Mahajan, A · 2019
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Mean Field Games and Applications: Numerical Aspects
Achdou, Y., Cardaliaguet, P., Delarue, F., Porretta, A., Santambrogio, F., Achdou, Y., and Laurière, M · 2020
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Language Evolution in Swarm Robotics: A Perspective
Cambier, N., Miletitch, R., Fremont, V., Dorigo, M., Ferrante, E., and Trianni, V · 2020
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Cao, H., Guo, X., and Laurière, M · 2020
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On the Convergence of Model Free Learning in Mean Field Games
Elie, R., Pérolat, J., Laurière, M., Geist, M., and Pietquin, O · 2020
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Revisiting Fundamentals of Experience Replay
Fedus, W., Ramachandran, P., Agarwal, R., Bengio, Y., Larochelle, H., Rowland, M., and Dabney, W · 2020
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Deep Learning Methods for Mean Field Control Problems With Delay
Fouque, J.-P. and Zhang, Z · 2020
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Multi Type Mean Field Reinforcement Learning
Ganapathi Subramanian, S., Poupart, P., Taylor, M. E., and Hegde, N · 2020
Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method
Guan, Y., Zou, S., Peng, H., Ni, W., Sun, Y., and Gao, H · 2023
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A General Framework for Learning Mean-Field Games
Guo, X., Hu, A., Xu, R., and Zhang, J · 2023
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Dynamic traffic signal control using mean field multi-agent reinforcement learning in large scale road-networks
Hu, T., hu, Z., Lu, Z., and Wen, X · 2023
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Partially Centralized Model-Predictive Mean Field Games for controlling multi-agent systems
Inoue, D., Ito, Y., Kashiwabara, T., Saito, N., and Yoshida, H · 2023
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How Well Do Reinforcement Learning Approaches Cope With Disruptions? The Case of Traffic Signal Control
Korecki, M., Dailisan, D., and Helbing, D · 2023
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A game-theoretic framework for autonomous vehicles velocity control: Bridging microscopic differential games and macroscopic mean field games
Huang, K., Di, X., Du, Q., and Chen, X · 2020
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Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
Mcaleer, S., Lanier, J., Fox, R., and Baldi, P · 2020
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Social diversity and social preferences in mixed-motive reinforcement learning
McKee, K. R., Gemp, I., McWilliams, B., Duéñez-Guzmán, E. A., Hughes, E., and Leibo, J. Z · 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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Munchausen Reinforcement Learning
Vieillard, N., Pietquin, O., and Geist, M · 2020
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Lv, Z., Xiao, L., Du, Y., Niu, G., Xing, C., and Xu, W · 2023
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Model-free Reinforcement Learning for Mean Field Games
Mishra, R., Vishwanath, S., and Vasal, D · 2023
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Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
Orr, J. and Dutta, A · 2023
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A mean field game approach for a class of linear quadratic discrete choice problems with congestion avoidance
Toumi, N., Malhame, R., and Le Ny, J · 2023
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Characterizing Speed Performance of Multi-Agent Reinforcement Learning
Wiggins, S., Meng, Y., Kannan, R., and Prasanna, V · 2023
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Mean Field Game and Broadcast Encryption-Based Joint Data Freshness Optimization and Privacy Preservation for Mobile Crowdsensing
Yang, Y., Zhang, B., Guo, D., Xu, R., Kumar, N., and Wang, W · 2023
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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
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Time-inconsistent mean-field stopping problems: A regularized equilibrium approach
Yu, X. and Yuan, F · 2023
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Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path
Zaman, M. A. U., Koppel, A., Bhatt, S., and Basar, T · 2023
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A mean field game model for timely computation in edge computing systems
Aggarwal, S., Bastopcu, M., Ulukus, S., Başar, T., et al · 2024
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Impact of Individual Defection on Collective Motion
Agrawal, S., Jhawar, J., Reina, A., Baliyarasimhuni, S. P., Hamann, H., and Li, L · 2024
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A semi-centralized multi-agent RL framework for efficient irrigation scheduling
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Regularity for Weak Solutions to First-Order Local Mean Field Games
Alharbi, A., Gomes, D., Di Fazio, G., and Ucer, M · 2024
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Common Noise by Random Measures: Mean-Field Equilibria for Competitive Investment and Hedging
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Partially observed mean-field game and related mean-field forward-backward stochastic differential equation
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Learning Discrete-Time Major-Minor Mean Field Games
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A Mean Field Game approach for pollution regulation of competitive firms
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Age of Information Minimization using Multi-agent UAVs based on AI-Enhanced Mean Field Resource Allocation
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Price Coordination for Electric Vehicle Fleet Using Mean Field Game Theory
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