Game-Theoretic Multiagent Reinforcement Learning

Leslie, D. S. and E. J. Collins (2006), ‘Generalised weakened fictitious play’. Games and Economic Behavior

2006

Cited alongside, same era.

Li, L., T. J. Walsh, and M. L. Littman (2006), ‘Towards a unified theory of state abstraction for MDPs.’. AI&M

2006

Cited alongside, same era.

McAleer, S., J. Lanier, R. Fox, and P. Baldi (2020), ‘Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games’. arXiv preprint arXiv:2006.08555

Original

2006

Cited alongside, same era.

Papoudakis, G., F. Christianos, L. Schäfer, and S. V. Albrecht (2020), ‘Comparative evaluation of multi-agent deep reinforcement learning algorithms’. arXiv preprint arXiv:2006.07869

Original

2006

Cited alongside, same era.

Schäfer, F., A. Anandkumar, and H. Owhadi (2020), ‘Competitive mirror descent’. arXiv preprint arXiv:2006.10179

Original

2006

Cited alongside, same era.

Steinberger, E., A. Lerer, and N. Brown (2020), ‘DREAM: Deep Regret minimization with Advantage baselines and Model-free learning’. arXiv preprint arXiv:2006.10410

Original

2006

Cited alongside, same era.

Šustr, M., M. Schmid, M. Moravčík, N. Burch, M. Lanctot, and M. Bowling (2020), ‘Sound search in imperfect information games’. arXiv preprint arXiv:2006.08740

Original

2006

Cited alongside, same era.

Nedic, A., A. Olshevsky, and W. Shi (2017), ‘Achieving geometric convergence for distributed optimization over time-varying graphs’. SIAM Journal on Optimization

2017

Later among the works it cites.

Neu, G., A. Jonsson, and V. Gómez (2017), ‘A unified view of entropy-regularized markov decision processes’. NIPS

2017

Later among the works it cites.

Pham, H. and X. Wei (2017), ‘Dynamic programming for optimal control of stochastic McKean–Vlasov dynamics’. SIAM Journal on Control and Optimization

2017

Later among the works it cites.

Pinto, L., J. Davidson, R. Sukthankar, and A. Gupta (2017), ‘Robust Adversarial Reinforcement Learning’. arXiv preprint arXiv:1703.02702

Original

2017

Later among the works it cites.

Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov (2017), ‘Proximal policy optimization algorithms’. arXiv preprint arXiv:1707.06347

Original

2017

Later among the works it cites.

Vinyals, O., T. Ewalds, S. Bartunov, P. Georgiev, A. S. Vezhnevets, M. Yeo, A. Makhzani, H. Küttler, J. Agapiou, J. Schrittwieser, et al. (2017), ‘StarCraft II: A new challenge for reinforcement learning’. arXiv preprint arXiv:1708.04782

Original

2017

Later among the works it cites.

Wei, C.-Y., Y.-T. Hong, and C.-J. Lu (2017), ‘Online reinforcement learning in stochastic games’. In: Advances in Neural Information Processing Systems

2017

Later among the works it cites.

Bailey, J. P. and G. Piliouras (2018), ‘Multiplicative weights update in zero-sum games’. In: Proceedings of the 2018 ACM Conference on Economics and Computation

2018

Later among the works it cites.

Brown, N. and T. Sandholm (2018), ‘Superhuman AI for heads-up no-limit poker: Libratus beats top professionals’. Science

2018

Later among the works it cites.

Brown, N., T. Sandholm, and B. Amos (2018), ‘Depth-limited solving for imperfect-information games’. Advances in neural information processing systems

2018

Later among the works it cites.

Cardaliaguet, P. and C.-A. Lehalle (2018), ‘Mean field game of controls and an application to trade crowding’. Mathematics and Financial Economics

2018

Later among the works it cites.

Carmona, R., F. Delarue, et al. (2018), Probabilistic Theory of Mean Field Games with Applications I-II

2018

Later among the works it cites.

Celli, A. and N. Gatti (2018), ‘Computational Results for Extensive-Form Adversarial Team Games’. In: AAAI Conference on Artificial Intelligence (AAAI)

2018

Later among the works it cites.

Dibangoye, J. and O. Buffet (2018), ‘Learning to Act in Decentralized Partially Observable MDPs’. In: International Conference on Machine Learning

2018

Later among the works it cites.

Farina, G., A. Celli, N. Gatti, and T. Sandholm (2018), ‘Ex ante coordination and collusion in zero-sum multi-player extensive-form games’. In: Advances in Neural Information Processing Systems

2018

Later among the works it cites.

Fazel, M., R. Ge, S. Kakade, and M. Mesbahi (2018), ‘Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator’. In: International Conference on Machine Learning

2018

Later among the works it cites.

Foerster, J. N., G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson (2018b), ‘Counterfactual Multi-Agent Policy Gradients’. In: S. A. McIlraith and K. Q. Weinberger (eds.): Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018

2018

Later among the works it cites.

Grau-Moya, J., F. Leibfried, and H. Bou-Ammar (2018), ‘Balancing two-player stochastic games with soft q-learning’. arXiv preprint arXiv:1802.03216

Original

2018

Later among the works it cites.

Haarnoja, T., A. Zhou, P. Abbeel, and S. Levine (2018), ‘Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor’. In: International Conference on Machine Learning

2018

Later among the works it cites.

Kroer, C. and T. Sandholm (2018), ‘A Unified Framework for Extensive-Form Game Abstraction with Bounds’. In: AI 3 Workshop at IJCAI

2018

Later among the works it cites.

Lacker, D. (2018), ‘On the convergence of closed-loop Nash equilibria to the mean field game limit’. arXiv preprint arXiv:1808.02745

Original

2018

Later among the works it cites.

Levine, S. (2018), ‘Reinforcement learning and control as probabilistic inference: Tutorial and review’. arXiv preprint arXiv:1805.00909

Original

2018

Later among the works it cites.

Li, Z. and A. Tewari (2018), ‘Sampled fictitious play is Hannan consistent’. Games and Economic Behavior

2018

Later among the works it cites.

Liu, X.-Y., Z. Xiong, S. Zhong, H. Yang, and A. Walid (2018), ‘Practical deep reinforcement learning approach for stock trading’. arXiv preprint arXiv:1811.07522

Original

2018

Later among the works it cites.

Macua, S. V., J. Zazo, and S. Zazo (2018), ‘Learning Parametric Closed-Loop Policies for Markov Potential Games’. In: International Conference on Learning Representations

2018

Later among the works it cites.

Mazumdar, E., L. J. Ratliff, and S. Sastry (2018), ‘On the convergence of gradient-based learning in continuous games’. arXiv preprint arXiv:1804.05464

Original

2018

Later among the works it cites.

Mertikopoulos, P., B. Lecouat, H. Zenati, C.-S. Foo, V. Chandrasekhar, and G. Piliouras (2018), ‘Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile’. arXiv preprint arXiv:1807.02629

Original

2018

Later among the works it cites.

Mescheder, L., A. Geiger, and S. Nowozin (2018), ‘Which training methods for GANs do actually converge?’. arXiv preprint arXiv:1801.04406

Original

2018

Later among the works it cites.

Pachocki, J., G. Brockman, J. Raiman, S. Zhang, H. Pondé, J. Tang, F. Wolski, C. Dennison, R. Jozefowicz, P. Debiak, et al. (2018), ‘OpenAI Five, 2018’. URL https://blog. openai. com/openai-five

2018

Later among the works it cites.

Perolat, J., B. Piot, and O. Pietquin (2018), ‘Actor-critic fictitious play in simultaneous move multistage games’. In: International Conference on Artificial Intelligence and Statistics

2018

Later among the works it cites.

Pham, H. and X. Wei (2018), ‘Bellman equation and viscosity solutions for mean-field stochastic control problem’. ESAIM: Control, Optimisation and Calculus of Variations

2018

Later among the works it cites.

Rafique, H., M. Liu, Q. Lin, and T. Yang (2018), ‘Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning’. arXiv

2018

Later among the works it cites.

Rashid, T., M. Samvelyan, C. S. De Witt, G. Farquhar, J. Foerster, and S. Whiteson (2018), ‘QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning’. arXiv preprint arXiv:1803.11485

Original

2018

Later among the works it cites.

Rothfuss, J., D. Lee, I. Clavera, T. Asfour, and P. Abbeel (2018), ‘ProMP: Proximal Meta-Policy Search’. In: International Conference on Learning Representations

2018

Later among the works it cites.

Saldi, N., T. Basar, and M. Raginsky (2018), ‘Markov–Nash Equilibria in Mean-Field Games with Discounted Cost’. SIAM Journal on Control and Optimization

2018

Later among the works it cites.

Sidford, A., M. Wang, X. Wu, L. Yang, and Y. Ye (2018), ‘Near-optimal time and sample complexities for solving Markov decision processes with a generative model’. In: Advances in Neural Information Processing Systems

2018

Later among the works it cites.

Silver, D., T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al. (2018), ‘A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play’. Science

2018

Later among the works it cites.

Song, M., A. Montanari, and P. Nguyen (2018), ‘A mean field view of the landscape of two-layers neural networks’. Proceedings of the National Academy of Sciences

2018

Later among the works it cites.

Srinivasan, S., M. Lanctot, V. Zambaldi, J. Pérolat, K. Tuyls, R. Munos, and M. Bowling (2018), ‘Actor-critic policy optimization in partially observable multiagent environments’. In: Advances in neural information processing systems

2018

Later among the works it cites.

Wei, H., G. Zheng, H. Yao, and Z. Li (2018), ‘Intellilight: A reinforcement learning approach for intelligent traffic light control’. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2018

Later among the works it cites.

Wen, Y., Y. Yang, R. Luo, J. Wang, and W. Pan (2018), ‘Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning’. In: International Conference on Learning Representations

2018

Later among the works it cites.

Zhang, K., Z. Yang, and T. Basar (2018a), ‘Networked multi-agent reinforcement learning in continuous spaces’. In: 2018 IEEE CDC

2018

Later among the works it cites.

Adolphs, L., H. Daneshmand, A. Lucchi, and T. Hofmann (2019), ‘Local saddle point optimization: A curvature exploitation approach’. In: The 22nd International Conference on Artificial Intelligence and Statistics

2019

Later among the works it cites.

Brown, N., A. Lerer, S. Gross, and T. Sandholm (2019), ‘Deep counterfactual regret minimization’. In: International Conference on Machine Learning

2019

Later among the works it cites.

Brown, N. and T. Sandholm (2019), ‘Superhuman AI for multiplayer poker’. Science

2019

Later among the works it cites.

Cecchin, A., P. D. Pra, M. Fischer, and G. Pelino (2019), ‘On the convergence problem in mean field games: a two state model without uniqueness’. SIAM Journal on Control and Optimization

2019

Later among the works it cites.

Cheung, Y. K. and G. Piliouras (2019), ‘Vortices instead of equilibria in minmax optimization: Chaos and butterfly effects of online learning in zero-sum games’. In: Conference on Learning Theory

2019

Later among the works it cites.

Da Silva, F. L. and A. H. R. Costa (2019), ‘A survey on transfer learning for multiagent reinforcement learning systems’. Journal of Artificial Intelligence Research

2019

Later among the works it cites.

Derakhshan, F. and S. Yousefi (2019), ‘A review on the applications of multiagent systems in wireless sensor networks’. International Journal of Distributed Sensor Networks

2019

Later among the works it cites.

Guo, X., A. Hu, R. Xu, and J. Zhang (2019), ‘Learning mean-field games’. In: Advances in Neural Information Processing Systems

2019

Later among the works it cites.

Hadikhanloo, S. and F. J. Silva (2019), ‘Finite mean field games: fictitious play and convergence to a first order continuous mean field game’. Journal de Mathématiques Pures et Appliquées

2019

Later among the works it cites.

Hernandez-Leal, P., B. Kartal, and M. E. Taylor (2019), ‘A survey and critique of multiagent deep reinforcement learning’. Autonomous Agents and Multi-Agent Systems

2019

Later among the works it cites.

Jaderberg, M., W. M. Czarnecki, I. Dunning, L. Marris, G. Lever, A. G. Castaneda, C. Beattie, N. C. Rabinowitz, A. S. Morcos, A. Ruderman, et al. (2019), ‘Human-level performance in 3D multiplayer games with population-based reinforcement learning’. Science

2019

Later among the works it cites.

Lacker, D. and T. Zariphopoulou (2019), ‘Mean field and n-agent games for optimal investment under relative performance criteria’. Mathematical Finance

2019

Later among the works it cites.

Liang, T. and J. Stokes (2019), ‘Interaction matters: A note on non-asymptotic local convergence of generative adversarial networks’. In: The 22nd International Conference on Artificial Intelligence and Statistics

2019

Later among the works it cites.

Mertikopoulos, P. and Z. Zhou (2019), ‘Learning in games with continuous action sets and unknown payoff functions’. Mathematical Programming

2019

Later among the works it cites.

Rosenberg, A. and Y. Mansour (2019), ‘Online Convex Optimization in Adversarial Markov Decision Processes’. In: International Conference on Machine Learning

2019

Later among the works it cites.

Saldi, N., T. Başar, and M. Raginsky (2019), ‘Approximate Nash equilibria in partially observed stochastic games with mean-field interactions’. Mathematics of Operations Research

2019

Later among the works it cites.

Schaefer, F. and A. Anandkumar (2019), ‘Competitive Gradient Descent’. In: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (eds.): Advances in Neural Information Processing Systems

2019

Later among the works it cites.

Schmid, M., N. Burch, M. Lanctot, M. Moravcik, R. Kadlec, and M. Bowling (2019), ‘Variance reduction in monte carlo counterfactual regret minimization (VR-MCCFR) for extensive form games using baselines’. In: Proceedings of the AAAI Conference on Artificial Intelligence

2019

Later among the works it cites.

Shi, W., S. Song, and C. Wu (2019), ‘Soft policy gradient method for maximum entropy deep reinforcement learning’. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence

2019

Later among the works it cites.

Son, K., D. Kim, W. J. Kang, D. E. Hostallero, and Y. Yi (2019), ‘Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning’. In: International Conference on Machine Learning

2019

Later among the works it cites.

Subramanian, J. and A. Mahajan (2019), ‘Reinforcement learning in stationary mean-field games’. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems

2019

Later among the works it cites.

Swenson, B. and H. V. Poor (2019), ‘Smooth Fictitious Play in N × \times 2 Potential Games’. In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers

2019

Later among the works it cites.

Tessler, C., Y. Efroni, and S. Mannor (2019), ‘Action robust reinforcement learning and applications in continuous control’. In: International Conference on Machine Learning

2019

Later among the works it cites.

Thekumparampil, K. K., P. Jain, P. Netrapalli, and S. Oh (2019), ‘Efficient algorithms for smooth minimax optimization’. In: Advances in Neural Information Processing Systems

2019

Later among the works it cites.

Vlatakis-Gkaragkounis, E.-V., L. Flokas, and G. Piliouras (2019), ‘Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games’. In: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (eds.): Advances in Neural Information Processing Systems

2019

Later among the works it cites.

Wang, L., Q. Cai, Z. Yang, and Z. Wang (2019), ‘Neural Policy Gradient Methods: Global Optimality and Rates of Convergence’. In: International Conference on Learning Representations

2019

Later among the works it cites.

Wen, Y., Y. Yang, R. Luo, and J. Wang (2019), ‘Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning’. IJCAI

2019

Later among the works it cites.

Zhang, Y. and M. M. Zavlanos (2019), ‘Distributed off-policy actor-critic reinforcement learning with policy consensus’. In: 2019 IEEE 58th Conference on Decision and Control (CDC)

2019

Later among the works it cites.

Zhou, M., Y. Chen, Y. Wen, Y. Yang, Y. Su, W. Zhang, D. Zhang, and J. Wang (2019), ‘Factorized Q-learning for large-scale multi-agent systems’. In: Proceedings of the First International Conference on Distributed Artificial Intelligence

2019

Later among the works it cites.

Bai, Y. and C. Jin (2020), ‘Provable self-play algorithms for competitive reinforcement learning’. In: International conference on machine learning

2020

Closest in time.

Bai, Y., C. Jin, and T. Yu (2020), ‘Near-optimal reinforcement learning with self-play’. Advances in neural information processing systems

2020

Closest in time.

Brown, N., A. Bakhtin, A. Lerer, and Q. Gong (2020), ‘Combining deep reinforcement learning and search for imperfect-information games’. Advances in Neural Information Processing Systems

2020

Closest in time.

Cheung, W. C., D. Simchi-Levi, and R. Zhu (2020), ‘Reinforcement learning for non-stationary markov decision processes: The blessing of (more) optimism’. ICML

2020

Closest in time.

Daskalakis, C., D. J. Foster, and N. Golowich (2020), ‘Independent policy gradient methods for competitive reinforcement learning’. Advances in neural information processing systems

2020

Closest in time.

Delarue, F. and R. F. Tchuendom (2020), ‘Selection of equilibria in a linear quadratic mean-field game’. Stochastic Processes and their Applications

2020

Closest in time.

Domingo-Enrich, C., S. Jelassi, A. Mensch, G. Rotskoff, and J. Bruna (2020), ‘A mean-field analysis of two-player zero-sum games’. Advances in neural information processing systems

2020

Closest in time.

Elie, R., J. Pérolat, M. Laurière, M. Geist, and O. Pietquin (2020), ‘On the Convergence of Model Free Learning in Mean Field Games.’. In: AAAI

2020

Closest in time.

Fiez, T., B. Chasnov, and L. Ratliff (2020), ‘Implicit learning dynamics in stackelberg games: Equilibria characterization, convergence analysis, and empirical study’. In: International Conference on Machine Learning

2020

Closest in time.

Golowich, N., S. Pattathil, C. Daskalakis, and A. Ozdaglar (2020), ‘Last iterate is slower than averaged iterate in smooth convex-concave saddle point problems’. In: Conference on Learning Theory

2020

Closest in time.

Haydari, A. and Y. Yılmaz (2020), ‘Deep reinforcement learning for intelligent transportation systems: A survey’. IEEE Transactions on Intelligent Transportation Systems

2020

Closest in time.

Ibrahim, A., W. Azizian, G. Gidel, and I. Mitliagkas (2020), ‘Linear lower bounds and conditioning of differentiable games’. In: International conference on machine learning

2020

Closest in time.

Letcher, A. (2018), ‘Stability and exploitation in differentiable games’. Ph.D. thesis, Master’s thesis, University of Oxford. Accessed: 2020-06-23

2020

Closest in time.

Lin, T., C. Jin, and M. Jordan (2020), ‘On gradient descent ascent for nonconvex-concave minimax problems’. In: International Conference on Machine Learning

2020

Closest in time.

Loizou, N., H. Berard, A. Jolicoeur-Martineau, P. Vincent, S. Lacoste-Julien, and I. Mitliagkas (2020), ‘Stochastic hamiltonian gradient methods for smooth games’. In: International Conference on Machine Learning

2020

Closest in time.

Michael, D. (2020), ‘Algorithmic Game Theory Lecture Notes’. http://www.cs.jhu.edu/ mdinitz/classes/AGT/Spring2020/Lectures/lecture6.pdf

2020

Closest in time.

Nguyen, T. T., N. D. Nguyen, and S. Nahavandi (2020), ‘Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications’. IEEE transactions on cybernetics

2020

Closest in time.

Nutz, M., J. San Martin, X. Tan, et al. (2020), ‘Convergence to the mean field game limit: a case study’. The Annals of Applied Probability

2020

Closest in time.

Ortner, R., P. Gajane, and P. Auer (2020), ‘Variational regret bounds for reinforcement learning’. In: Uncertainty in Artificial Intelligence

2020

Closest in time.

Schrittwieser, J., I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, et al. (2020), ‘Mastering atari, go, chess and shogi by planning with a learned model’. Nature

2020

Closest in time.

Sidford, A., M. Wang, L. Yang, and Y. Ye (2020), ‘Solving discounted stochastic two-player games with near-optimal time and sample complexity’. In: International Conference on Artificial Intelligence and Statistics

2020

Closest in time.

Sirignano, J. and K. Spiliopoulos (2020), ‘Mean field analysis of neural networks: A law of large numbers’. SIAM Journal on Applied Mathematics

2020

Closest in time.

uz Zaman, M. A., K. Zhang, E. Miehling, and T. Başar (2020), ‘Approximate equilibrium computation for discrete-time linear-quadratic mean-field games’. In: 2020 American Control Conference (ACC)

2020

Closest in time.

Xie, Q., Y. Chen, Z. Wang, and Z. Yang (2020), ‘Learning zero-sum simultaneous-move markov games using function approximation and correlated equilibrium’. In: Conference on learning theory

2020

Closest in time.

Yang, Y., Y. Wen, L. Chen, J. Wang, K. Shao, D. Mguni, and W. Zhang (2020), ‘Multi-Agent Determinantal Q-Learning’

2020

Closest in time.

Zhang, B. and T. Sandholm (2020), ‘Small Nash equilibrium certificates in very large games’. Advances in Neural Information Processing Systems

2020

Closest in time.

Barazandeh, B., D. A. Tarzanagh, and G. Michailidis (2021), ‘Solving a class of non-convex min-max games using adaptive momentum methods’. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2021

Closest in time.

Cen, S., Y. Wei, and Y. Chi (2021), ‘Fast policy extragradient methods for competitive games with entropy regularization’. Advances in Neural Information Processing Systems

2021

Closest in time.

Chen, Z., D. Zhou, and Q. Gu (2021), ‘Almost optimal algorithms for two-player markov games with linear function approximation’. arXiv preprint arXiv:2102.07404

Original

2021

Closest in time.

Daskalakis, C., M. Fishelson, and N. Golowich (2021), ‘Near-optimal no-regret learning in general games’. Advances in Neural Information Processing Systems

2021

Closest in time.

Deng, Y. and M. Mahdavi (2021), ‘Local stochastic gradient descent ascent: Convergence analysis and communication efficiency’. In: International Conference on Artificial Intelligence and Statistics

2021

Closest in time.

Domingues, O. D., P. Ménard, E. Kaufmann, and M. Valko (2021), ‘Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited’. In: Algorithmic Learning Theory

2021

Closest in time.

Fasoulakis, M., E. Markakis, Y. Pantazis, and C. Varsos (2021), ‘Forward Looking Best-Response Multiplicative Weights Update Methods’. arXiv preprint arXiv:2106.03579

Original

2021

Closest in time.

Flokas, L., E.-V. Vlatakis-Gkaragkounis, and G. Piliouras (2021), ‘Solving min-max optimization with hidden structure via gradient descent ascent’. arXiv preprint arXiv:2101.05248

Original

2021

Closest in time.

Franci, B. and S. Grammatico (2021), ‘Training generative adversarial networks via stochastic Nash games’. IEEE Transactions on Neural Networks and Learning Systems

2021

Closest in time.

Fu, H., W. Liu, S. Wu, Y. Wang, T. Yang, K. Li, J. Xing, B. Li, B. Ma, Q. Fu, et al. (2021), ‘Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game’. In: International Conference on Learning Representations

2021

Closest in time.

Gao, B. and L. Pavel (2021), ‘Second-Order Mirror Descent: Convergence in Games Beyond Averaging and Discounting’. arXiv preprint arXiv:2111.09982

Original

2021

Closest in time.

Grnarova, P., Y. Kilcher, K. Y. Levy, A. Lucchi, and T. Hofmann (2021), ‘Generative minimization networks: Training GANs without competition’. arXiv preprint arXiv:2103.12685

Original

2021

Closest in time.

He, H., S. Zhao, Y. Xi, and J. Ho (2021), ‘AGE: Enhancing the Convergence on GANs using Alternating extra-gradient with Gradient Extrapolation’. In: NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications

2021

Closest in time.

Kalogiannis, F., E.-V. Vlatakis-Gkaragkounis, and I. Panageas (2021), ‘Teamwork makes von Neumann work: Min-Max Optimization in Two-Team Zero-Sum Games’. arXiv preprint arXiv:2111.04178

Original

2021

Closest in time.

Kozuno, T., P. Ménard, R. Munos, and M. Valko (2021), ‘Learning in two-player zero-sum partially observable Markov games with perfect recall’. Advances in Neural Information Processing Systems

2021

Closest in time.

Li, C. J., Y. Yu, N. Loizou, G. Gidel, Y. Ma, N. L. Roux, and M. I. Jordan (2021), ‘On the convergence of stochastic extragradient for bilinear games with restarted iteration averaging’. arXiv preprint arXiv:2107.00464

Original

2021

Closest in time.

Li, H. (2021), ‘On the Complexity of Nonconvex-Strongly-Concave Smooth Minimax Optimization Using First-Order Methods’. Ph.D. thesis, Massachusetts Institute of Technology

2021

Closest in time.

Liao, L., L. Shen, J. Duan, M. Kolar, and D. Tao (2021), ‘Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems’. arXiv preprint arXiv:2106.10022

Original

2021

Closest in time.

Liu, Q., T. Yu, Y. Bai, and C. Jin (2021), ‘A sharp analysis of model-based reinforcement learning with self-play’. In: International Conference on Machine Learning

2021

Closest in time.

Loizou, N., H. Berard, G. Gidel, I. Mitliagkas, and S. Lacoste-Julien (2021), ‘Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity’. Advances in Neural Information Processing Systems

2021

Closest in time.

McAleer, S., J. Lanier, P. Baldi, and R. Fox (2021), ‘XDO: A double oracle algorithm for extensive-form games’. Advances in Neural Information Processing Systems (NeurIPS)

2021

Closest in time.

Milec, D., O. Kubíček, and V. Lisỳ (2021), ‘Continual Depth-limited Responses for Computing Counter-strategies in Sequential Games’. arXiv preprint arXiv:2112.12594

Original

2021

Closest in time.

Mladenovic, A., I. Sakos, G. Gidel, and G. Piliouras (2021), ‘Generalized Natural Gradient Flows in Hidden Convex-Concave Games and GANs’. In: International Conference on Learning Representations

2021

Closest in time.

Perolat, J., R. Munos, J.-B. Lespiau, S. Omidshafiei, M. Rowland, P. Ortega, N. Burch, T. Anthony, D. Balduzzi, B. De Vylder, et al. (2021), ‘From Poincaré recurrence to convergence in imperfect information games: Finding equilibrium via regularization’. In: International Conference on Machine Learning

2021

Closest in time.

Qi, H., F. Li, S. Tan, and X. Zhang (2021), ‘Training Generative Adversarial Networks with Adaptive Composite Gradient’. arXiv preprint arXiv:2111.05508

Original

2021

Closest in time.

Song, Z., S. Mei, and Y. Bai (2021), ‘When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?’. arXiv preprint arXiv:2110.04184

Original

2021

Closest in time.

Xu, Z., J. Shen, Z. Wang, and Y. Dai (2021), ‘Zeroth-order alternating randomized gradient projection algorithms for general nonconvex-concave minimax problems’. arXiv preprint arXiv:2108.00473

Original

2021

Closest in time.

Anagnostides, I. and I. Panageas (2022), ‘Frequency-Domain Representation of First-Order Methods: A Simple and Robust Framework of Analysis’. In: Symposium on Simplicity in Algorithms (SOSA)

2022

Closest in time.

Bai, Y., C. Jin, S. Mei, and T. Yu (2022), ‘Near-optimal learning of extensive-form games with imperfect information’. In: International Conference on Machine Learning

2022

Closest in time.

Böhm, A. (2022), ‘Solving Nonconvex-Nonconcave Min-Max Problems exhibiting Weak Minty Solutions’. arXiv preprint arXiv:2201.12247

Original

2022

Closest in time.

BoHERE!HERE!hm, A., M. Sedlmayer, E. R. Csetnek, and R. I. Bot (2022), ‘Two Steps at a Time—Taking GAN Training in Stride with Tseng’s Method’. SIAM Journal on Mathematics of Data Science

2022

Closest in time.

Degrave, J., F. Felici, J. Buchli, M. Neunert, B. Tracey, F. Carpanese, T. Ewalds, R. Hafner, A. Abdolmaleki, D. de Las Casas, et al. (2022), ‘Magnetic control of tokamak plasmas through deep reinforcement learning’. Nature

2022

Closest in time.

Ding, Z., D. Su, Q. Liu, and C. Jin (2022), ‘A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games’. arXiv preprint arXiv:2207.08894

Original

2022

Closest in time.

Doan, T. (2022), ‘Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems’. In: Learning for Dynamics and Control Conference

2022

Closest in time.

Du, Y., C. Ma, Y. Liu, R. Lin, H. Dong, J. Wang, and Y. Yang (2022b), ‘Scalable model-based policy optimization for decentralized networked systems’. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2022

Closest in time.

(FAIR)†, M. F. A. R. D. T., A. Bakhtin, N. Brown, E. Dinan, G. Farina, C. Flaherty, D. Fried, A. Goff, J. Gray, H. Hu, et al. (2022), ‘Human-level play in the game of Diplomacy by combining language models with strategic reasoning’. Science

2022

Closest in time.

Farina, G. and T. Sandholm (2022), ‘Fast payoff matrix sparsification techniques for structured extensive-form games’. In: Proceedings of the AAAI Conference on Artificial Intelligence

2022

Closest in time.

Gorbunov, E., N. Loizou, and G. Gidel (2022), ‘Extragradient method: O (1/K) last-iterate convergence for monotone variational inequalities and connections with cocoercivity’. In: International Conference on Artificial Intelligence and Statistics

2022

Closest in time.

Ha, J. and G. Kim (2022), ‘On Convergence of Lookahead in Smooth Games’. In: International Conference on Artificial Intelligence and Statistics

2022

Closest in time.

Harris, K., I. Anagnostides, G. Farina, M. Khodak, Z. S. Wu, and T. Sandholm (2022), ‘Meta-learning in games’. arXiv preprint arXiv:2209.14110

Original

2022

Closest in time.

He, H., S. Zhao, Y. Xi, J. Ho, and Y. Saad (2022), ‘GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING’. In: International Conference on Learning Representations

2022

Closest in time.

Mao, W. and T. Başar (2022), ‘Provably efficient reinforcement learning in decentralized general-sum markov games’. Dynamic Games and Applications

2022

Closest in time.

Perolat, J., B. De Vylder, D. Hennes, E. Tarassov, F. Strub, V. de Boer, P. Muller, J. T. Connor, N. Burch, T. Anthony, et al. (2022), ‘Mastering the game of Stratego with model-free multiagent reinforcement learning’. Science

2022

Closest in time.

Qu, G., A. Wierman, and N. Li (2022), ‘Scalable reinforcement learning for multiagent networked systems’. Operations Research

2022

Closest in time.

Sharma, P., R. Panda, G. Joshi, and P. Varshney (2022), ‘Federated minimax optimization: Improved convergence analyses and algorithms’. In: International Conference on Machine Learning

2022

Closest in time.

Sokota, S., R. D’Orazio, J. Z. Kolter, N. Loizou, M. Lanctot, I. Mitliagkas, N. Brown, and C. Kroer (2022), ‘A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games’. arXiv preprint arXiv:2206.05825

Original

2022

Closest in time.

Song, Z., S. Mei, and Y. Bai (2022), ‘Sample-efficient learning of correlated equilibria in extensive-form games’. Advances in Neural Information Processing Systems

2022

Closest in time.

Vyas, A. and K. Azizzadenesheli (2022), ‘Competitive Gradient Optimization’. arXiv preprint arXiv:2205.14232

Original

2022

Closest in time.

Wibisono, A., M. Tao, and G. Piliouras (2022), ‘Alternating Mirror Descent for Constrained Min-Max Games’. arXiv preprint arXiv:2206.04160

Original

2022

Closest in time.

Yang, J., A. Orvieto, A. Lucchi, and N. He (2022), ‘Faster single-loop algorithms for minimax optimization without strong concavity’. In: International Conference on Artificial Intelligence and Statistics

2022

Closest in time.

Yoon, T. and E. K. Ryu (2022), ‘Accelerated Minimax Algorithms Flock Together’. arXiv preprint arXiv:2205.11093

Original

2022

Closest in time.

Zhan, W., J. D. Lee, and Z. Yang (2022), ‘Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games’. arXiv preprint arXiv:2206.01588

Original

2022

Closest in time.

Zhang, B. and T. Sandholm (2022), ‘Polynomial-time optimal equilibria with a mediator in extensive-form games’. Advances in Neural Information Processing Systems

2022

Closest in time.

Fiegel, C., P. Ménard, T. Kozuno, R. Munos, V. Perchet, and M. Valko (2023), ‘Adapting to game trees in zero-sum imperfect information games’. In: International Conference on Machine Learning

2023

Closest in time.

Liu, W., H. Fu, Q. Fu, and Y. Wei (2023), ‘Opponent-limited online search for imperfect information games’. In: International Conference on Machine Learning

2023

Closest in time.

Ma, C., Z. Yang, M. Gao, H. Ci, J. Gao, X. Pan, and Y. Yang (2023), ‘Red teaming game: A game-theoretic framework for red teaming language models’. arXiv preprint arXiv:2310.00322

Original

2023

Closest in time.

McAleer, S., G. Farina, G. Zhou, M. Wang, Y. Yang, and T. Sandholm (2023), ‘Team-PSRO for learning approximate TMECor in large team games via cooperative reinforcement learning’. Advances in Neural Information Processing Systems

2023

Closest in time.

Meng, L., Z. Ge, W. Li, B. An, and Y. Gao (2023), ‘Efficient last-iterate convergence algorithms in solving games’. arXiv preprint arXiv:2308.11256

Original

2023

Closest in time.

Tang, X., S. M. McAleer, Y. Yang, et al. (2023), ‘Regret-minimizing double oracle for extensive-form games’. In: International Conference on Machine Learning

2023

Closest in time.

Yang, S., O. Nachum, Y. Du, J. Wei, P. Abbeel, and D. Schuurmans (2023), ‘Foundation models for decision making: Problems, methods, and opportunities’. arXiv preprint arXiv:2303.04129

Original

2023

Closest in time.

Anagnostides, I., I. Panageas, G. Farina, and T. Sandholm (2024), ‘On the convergence of no-regret learning dynamics in time-varying games’. Advances in Neural Information Processing Systems

2024

Closest in time.

Fan, W., Z. Yu, C. Ma, C. Li, Y. Yang, and X. Zhang (2024), ‘Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning’. arXiv preprint arXiv:2410.15841

Original

2024

Closest in time.

Ge, Z., Z. Xu, T. Ding, W. Li, and Y. Gao (2024), ‘Efficient subgame refinement for extensive-form games’. Advances in Neural Information Processing Systems

2024

Closest in time.

Ma, C., A. Li, Y. Du, H. Dong, and Y. Yang (2024), ‘Efficient and scalable reinforcement learning for large-scale network control’. Nature Machine Intelligence

2024

Closest in time.

Martin, C. and T. Sandholm (2024), ‘Simultaneous incremental support adjustment and metagame solving: An equilibrium-finding framework for continuous-action games’. arXiv preprint arXiv:2406.08683

Original

2024

Closest in time.

Tang, X., C. Wang, C. Ma, I. Bogunovic, S. McAleer, and Y. Yang (2024), ‘Sample-Efficient Regret-Minimizing Double Oracle in Extensive-Form Games’. arXiv preprint arXiv:2411.00954

Original

2024

Closest in time.

Wang, M., C. Ma, Q. Chen, L. Meng, Y. Han, J. Xiao, Z. Zhang, J. Huo, W. J. Su, and Y. Yang (2024), ‘Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment’. arXiv preprint arXiv:2410.16714

Original

2024

Closest in time.

Xu, L., D. Perez-Liebana, and A. Dockhorn (2024b), ‘Strategy Game-Playing with Size-Constrained State Abstraction’. In: 2024 IEEE Conference on Games (CoG)

2024

Closest in time.

Zhang, R., Z. Xu, C. Ma, C. Yu, W.-W. Tu, S. Huang, D. Ye, W. Ding, Y. Yang, and Y. Wang (2024), ‘A Survey on Self-play Methods in Reinforcement Learning’. arXiv preprint arXiv:2408.01072

Original

2024

Closest in time.

Szepesvári, C. and M. L. Littman (1999), ‘A unified analysis of value-function-based reinforcement-learning algorithms’. Neural computation

2060

Closest in time.

Qu, G., Y. Lin, A. Wierman, and N. Li (2020), ‘Scalable multi-agent reinforcement learning for networked systems with average reward’. Advances in Neural Information Processing Systems

2086

Closest in time.

Sunehag, P., G. Lever, A. Gruslys, W. M. Czarnecki, V. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls, et al. (2018), ‘Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward’. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

2087

Closest in time.