Fetching the paper…
Reading the bibliography…
Learning in multi-agent systems is highly challenging due to several factors including the non-stationarity introduced by agents' interactions and the combinatorial nature of their state and action spaces.
L’hypothèse du champ moléculaire et la propriété ferromagnétique
Pierre Weiss · 1907
Earlier work this paper cites.
Linear-quadratic mean-field reinforcement learning: convergence of policy gradient methods
René Carmona, Mathieu Laurière, and Zongjun Tan · 1910
Earlier work this paper cites.
Model-free mean-field reinforcement learning: mean-field mdp and mean-field q-learning
René Carmona, Mathieu Laurière, and Zongjun Tan · 1910
Earlier work this paper cites.
On a space of totally additive functions
Leonid Kantorovich and Gennady S. Rubinstein · 1958
Earlier work this paper cites.
A near-optimal polynomial time algorithm for learning in certain classes of stochastic games
Ronen I Brafman and Moshe Tennenholtz · 2000
Earlier work this paper cites.
The theory of social functions: challenges for computational social science and multi-agent learning
Cristiano Castelfranchi · 2001
Earlier work this paper cites.
R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz · 2002
Earlier work this paper cites.
Stock trading system using reinforcement learning with cooperative agents
Jae Won Lee, Byoung-Tak Zhang, et al · 2002
Earlier work this paper cites.
Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
Earlier work this paper cites.
Large population stochastic dynamic games: closed-loop mckean-vlasov systems and the nash certainty equivalence principle
Minyi Huang, Roland P Malhamé, Peter E Caines, et al · 2006
Earlier work this paper cites.
Large-population cost-coupled lqg problems with nonuniform agents: individual-mass behavior and decentralized ϵ \epsilon -equilibria
Minyi Huang, Peter E Caines, and Roland P Malhamé · 2007
Earlier work this paper cites.
A multiagent approach to q q -learning for daily stock trading
J. W. Lee, J. Park, J. O, J. Lee, and E. Hong · 2007
Earlier work this paper cites.
A comprehensive survey of multiagent reinforcement learning
Lucian Buşoniu, Robert Babuška, and Bart De Schutter · 2008
Earlier work this paper cites.
Support vector machines
Ingo Steinwart and Christmann Andreas · 2008
Earlier work this paper cites.
Neural network learning: Theoretical foundations
Martin Anthony and Peter L Bartlett · 2009
Earlier work this paper cites.
Near-optimal regret bounds for reinforcement learning
Peter Auer, Thomas Jaksch, and Ronald Ortner · 2009
Earlier work this paper cites.
Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger · 2010
Earlier work this paper cites.
Learning in mean-field oscillator games
Huibing Yin, Prashant G Mehta, Sean P Meyn, and Uday V Shanbhag · 2010
Earlier work this paper cites.
Regret bounds for the adaptive control of linear quadratic systems
Yasin Abbasi-Yadkori and Csaba Szepesvaŕi · 2011
Earlier work this paper cites.
Mean field games and applications
Olivier Guéant, Jean-Michel Lasry, and Pierre-Louis Lions · 2011
Earlier work this paper cites.
Contextual Gaussian process bandit optimization
Andreas Krause and Cheng Soon Ong · 2011
Earlier work this paper cites.
Mean field for Markov decision processes: from discrete to continuous optimization
Nicolas Gast, Bruno Gaujal, and Jean-Yves Le Boudec · 2012
Earlier work this paper cites.
Mean field games and mean field type control theory , volume 101
Alain Bensoussan, Jens Frehse, Phillip Yam, et al · 2013
Earlier work this paper cites.
Probabilistic analysis of mean-field games
René Carmona and François Delarue · 2013
Earlier work this paper cites.
Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (marlin-atsc): Methodology and large-scale application on downtown toronto
S. El-Tantawy, B. Abdulhai, and H. Abdelgawad · 2013
Earlier work this paper cites.
Learning in mean-field games
Huibing Yin, Prashant G Mehta, Sean P Meyn, and Uday V Shanbhag · 2013
Cited alongside, same era.
Mean field games models—a brief survey
Diogo A Gomes et al · 2014
Cited alongside, same era.
Optimism-driven exploration for nonlinear systems
Teodor Mihai Moldovan, Sergey Levine, Michael I Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Cited alongside, same era.
Two numerical approaches to stationary mean-field games
Noha Almulla, Rita Ferreira, and Diogo Gomes · 2017
Cited alongside, same era.
Learning in mean field games: the fictitious play
Pierre Cardaliaguet and Saeed Hadikhanloo · 2017
Cited alongside, same era.
A review of cooperative multi-agent deep reinforcement learning
Afshin OroojlooyJadid and Davood Hajinezhad · 2019
Later among the works it cites.
Statistical Aspects of Wasserstein Distances
Victor M. Panaretos and Yoav Zemel · 2019
Later among the works it cites.
Reinforcement learning in stationary mean-field games
Jayakumar Subramanian and Aditya Mahajan · 2019
Later among the works it cites.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Later among the works it cites.
Multi-agent reinforcement learning: A selective overview of theories and algorithms
Kaiqing Zhang, Zhuoran Yang, and Tamer Başar · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On kernelized multi-armed bandits
Sayak Ray Chowdhury and Aditya Gopalan · 2017
Cited alongside, same era.
A survey of learning in multiagent environments: Dealing with non-stationarity
Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, and Enrique Munoz de Cote · 2017
Cited alongside, same era.
Limit theory for controlled mckean–vlasov dynamics
Daniel Lacker · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Multi-agent reinforcement learning in sequential social dilemmas
Joel Z. Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, and Thore Graepel · 2017
Cited alongside, same era.
Lower bounds on regret for noisy Gaussian process bandit optimization
Jonathan Scarlett, Ilija Bogunovic, and Volkan Cevher · 2017
Cited alongside, same era.
Efficient model-based reinforcement learning through optimistic policy search and planning
Sebastian Curi, Felix Berkenkamp, and Andreas Krause · 2020
Later among the works it cites.
Regret bounds for kernel-based reinforcement learning
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, and Michal Valko · 2020
Later among the works it cites.
On the convergence of model free learning in mean field games
Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, and Olivier Pietquin · 2020
Later among the works it cites.
Actor-critic provably finds nash equilibria of linear-quadratic mean-field games
Zuyue Fu, Zhuoran Yang, Yongxin Chen, and Zhaoran Wang · 2020
Later among the works it cites.
Dynamic programming principles for mean-field controls with learning
Haotian Gu, Xin Guo, Xiaoli Wei, and Renyuan Xu · 2020
Later among the works it cites.
A general framework for learning mean-field games
Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang · 2020
Later among the works it cites.
Provably efficient reinforcement learning with linear function approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang, and Michael I Jordan · 2020
Later among the works it cites.
Information theoretic regret bounds for online nonlinear control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, and Wen Sun · 2020
Later among the works it cites.
Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi · 2020
Later among the works it cites.
Breaking the curse of many agents: Provable mean embedding q-iteration for mean-field reinforcement learning
Lingxiao Wang, Zhuoran Yang, and Zhaoran Wang · 2020
Later among the works it cites.
Reinforcement Learning for Mean Field Games, with Applications to Economics
Andrea Angiuli, Jean-Pierre Fouque, and Mathieu Lauriere · 2021
Closest in time.
Mean Field Markov Decision Processes
Nicole Bäuerle · 2021
Closest in time.
Linear-quadratic zero-sum mean-field type games: Optimality conditions and policy optimization
René Carmona, Kenza Hamidouche, Mathieu LauriÉre, and Zongjun Tan · 2021
Closest in time.
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, and Tuo Zhao · 2021
Closest in time.
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, and Olivier Pietquin · 2021
Closest in time.
Mean-field controls with q-learning for cooperative marl: Convergence and complexity analysis
Haotian Gu, Xin Guo, Xiaoli Wei, and Renyuan Xu · 2021
Closest in time.
Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, and Hongyuan Zha · 2021
Closest in time.
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time, 2021
Weichen Wang, Jiequn Han, Zhuoran Yang, and Zhaoran Wang · 2021
Closest in time.
Unified reinforcement q-learning for mean field game and control problems
Andrea Angiuli, Jean-Pierre Fouque, and Mathieu Laurière · 2022
Closest in time.
Learning Mean Field Games: A Survey
Mathieu Laurière, Sarah Perrin, Matthieu Geist, and Olivier Pietquin · 2022
Closest in time.
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation, 2022
Pier Giuseppe Sessa, Maryam Kamgarpour, and Andreas Krause · 2022
Closest in time.