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Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information.
Non-cooperative games
John Nash · 1951
Earlier work this paper cites.
A social equilibrium existence theorem
Gerard Debreu · 1952
Earlier work this paper cites.
Fixed-point and minimax theorems in locally convex topological linear spaces
Ky Fan · 1952
Earlier work this paper cites.
A further generalization of the kakutani fixed point theorem, with application to nash equilibrium points
Irving L Glicksberg · 1952
Earlier work this paper cites.
Stochastic games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Equilibrium in a stochastic n n -person game
Arlington M Fink · 1964
Earlier work this paper cites.
Principles of mathematical analysis , volume 3
Walter Rudin et al · 1976
Earlier work this paper cites.
Introductory functional analysis with applications , volume 17
Erwin Kreyszig · 1991
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
Earlier work this paper cites.
Multiagent reinforcement learning: theoretical framework and an algorithm
Junling Hu, Michael P Wellman, et al · 1998
Earlier work this paper cites.
Introduction to reinforcement learning , volume 135
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
The complexity of decentralized control of markov decision processes
Daniel S Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein · 2002
Earlier work this paper cites.
Robust dynamic programming
Garud N Iyengar · 2005
Earlier work this paper cites.
Robust reinforcement learning
Jun Morimoto and Kenji Doya · 2005
Earlier work this paper cites.
Robust control of markov decision processes with uncertain transition matrices
Arnab Nilim and Laurent El Ghaoui · 2005
Earlier work this paper cites.
A comprehensive survey of multiagent reinforcement learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2008
Earlier work this paper cites.
Discounted robust stochastic games and an application to queueing control
Erim Kardeş, Fernando Ordóñez, and Randolph W Hall · 2011
Earlier work this paper cites.
Tractable objectives for robust policy optimization
Katherine Chen and Michael Bowling · 2012
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, et al · 2015
Earlier work this paper cites.
A concise introduction to decentralized POMDPs , volume 1
Frans A Oliehoek, Christopher Amato, et al · 2016
Earlier work this paper cites.
Vulnerability of deep reinforcement learning to policy induction attacks
Vahid Behzadan and Arslan Munir · 2017
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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Guided deep reinforcement learning for swarm systems
Maximilian Hüttenrauch and Adrian Šošić · 2017
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Delving into adversarial attacks on deep policies
Jernej Kos and Dawn Song · 2017
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Tactics of adversarial attack on deep reinforcement learning agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Adversarially robust policy learning: Active construction of physically-plausible perturbations
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Arnu Pretorius, Scott Cameron, et al · 2020
Later among the works it cites.
Scalable multi-agent reinforcement learning for networked systems with average reward
Guannan Qu, Yiheng Lin, Adam Wierman, and Na Li · 2020
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Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Gregory Farquhar, Bei Peng, and Shimon Whiteson · 2020
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Nonconvex min-max optimization: Applications, challenges, and recent theoretical advances
Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, and Mingyi Hong · 2020
Later among the works it cites.
Formulazero: Distributionally robust online adaptation via offline population synthesis
Aman Sinha, Matthew O’Kelly, et al · 2020
Later among the works it cites.
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Ajay Mandlekar, Yuke Zhu, Animesh Garg, Li Fei-Fei, and Silvio Savarese · 2017
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Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik and Zhenyi Tang · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, and Rahul Sukthankar · 2017
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Counterfactual multi-agent policy gradients
Jakob Foerster and Gregory Farquhar · 2018
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Enforcing signal temporal logic specifications in multi-agent adversarial environments: A deep q-learning approach
Devaprakash Muniraj, Kyriakos G Vamvoudakis, and Mazen Farhood · 2018
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Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, et al · 2018
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Robust multi-agent reinforcement learning with social empowerment for coordination and communication
Tessa van der Heiden, C Salge, Efstratios Gavves, and H van Hoof · 2020
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Learning implicit credit assignment for cooperative multi-agent reinforcement learning
Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, and Yuk Ying Chung · 2020
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Certifiable robustness to adversarial state uncertainty in deep reinforcement learning
Michael Everett, Björn Lütjens, and Jonathan P How · 2021
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V-learning–a simple, efficient, decentralized algorithm for multiagent rl
Chi Jin, Qinghua Liu, Yuanhao Wang, and Tiancheng Yu · 2021
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Ss-sfda: Self-supervised source-free domain adaptation for road segmentation in hazardous environments
Divya Kothandaraman, Rohan Chandra, and Dinesh Manocha · 2021
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Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions
Ze Liu, Yingfeng Cai, Hai Wang, Long Chen, Hongbo Gao, Yunyi Jia, and Yicheng Li · 2021
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Robust opponent modeling via adversarial ensemble reinforcement learning
Macheng Shen and Jonathan P How · 2021
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Value-decomposition multi-agent actor-critics
Jianyu Su, Stephen Adams, and Peter Beling · 2021
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Romax: Certifiably robust deep multiagent reinforcement learning via convex relaxation
Chuangchuang Sun, Dong-Ki Kim, and Jonathan P How · 2021
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Robust reinforcement learning: A constrained game-theoretic approach
Jing Yu, Clement Gehring, Florian Schäfer, and Animashree Anandkumar · 2021
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Robust reinforcement learning on state observations with learned optimal adversary
Huan Zhang, Hongge Chen, Duane Boning, and Cho-Jui Hsieh · 2021
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Efficient adversarial training without attacking: Worst-case-aware robust reinforcement learning
Yongyuan Liang, Yanchao Sun, Ruijie Zheng, and Furong Huang · 2022
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Certifiably robust policy learning against adversarial communication in multi-agent systems
Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, and Furong Huang · 2022
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The surprising effectiveness of ppo in cooperative multi-agent games
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Robust multi-agent reinforcement learning with state uncertainty
Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, and Fei Miao · 2023
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Robust reinforcement learning via adversarial kernel approximation
Kaixin Wang, Uri Gadot, Navdeep Kumar, Kfir Levy, and Shie Mannor · 2023
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