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Ensuring safety in MARL, particularly when deploying it in real-world applications such as autonomous driving, emerges as a critical challenge.
A multiple leader stackelberg model and analysis
Hanif D Sherali · 1984
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Multi-agent reinforcement learning: Independent vs. cooperative agents
Ming Tan · 1993
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Constrained Markov Decision Processes
E. Altman · 1999
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Nash q-learning for general-sum stochastic games
Junling Hu and Michael P. Wellman · 2003
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Markov Decision Processes
William Uther · 2010
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Market structure and equilibrium
Heinrich Von Stackelberg · 2010
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A comprehensive survey on safe reinforcement learning
Javier García, Fern, and o Fernández · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 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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The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
Philip Polack, Florent Altché, Brigitte d’Andréa Novel, and Arnaud de La Fortelle · 2017
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An environment for autonomous driving decision-making
Edouard Leurent · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2018
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Gradient methods for solving stackelberg games, 2019
Roi Naveiro and David Ríos Insua · 2019
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Reward constrained policy optimization
Chen Tessler, Daniel J. Mankowitz, and Shie Mannor · 2019
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Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2020
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Bi-level actor-critic for multi-agent coordination
Haifeng Zhang, Weizhe Chen, Zeren Huang, Minne Li, Yaodong Yang, Weinan Zhang, and Jun Wang · 2020
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Safe multi-agent reinforcement learning through decentralized multiple control barrier functions
A review of safe reinforcement learning: Methods, theory and applications
Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Yaodong Yang, and Alois Knoll · 2022
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Robust reinforcement learning as a stackelberg game via adaptively-regularized adversarial training, 2022
Peide Huang, Mengdi Xu, Fei Fang, and Ding Zhao · 2022
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Trust region policy optimisation in multi-agent reinforcement learning
JG Kuba, R Chen, M Wen, Y Wen, F Sun, J Wang, and Y Yang · 2022
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Shield decentralization for safe multi-agent reinforcement learning
Daniel Melcer, Christopher Amato, and Stavros Tripakis · 2022
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Learning in bi-level markov games
Linghui Meng, Jingqing Ruan, Dengpeng Xing, and Bo Xu · 2022
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Zhiyuan Cai, Huanhui Cao, Wenjie Lu, Lin Zhang, and Hao Xiong · 2021
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Safe multi-agent reinforcement learning via shielding
Ingy ElSayed-Aly, Suda Bharadwaj, Christopher Amato, Rüdiger Ehlers, Ufuk Topcu, and Lu Feng · 2021
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Settling the variance of multi-agent policy gradients
Jakub Grudzien Kuba, Muning Wen, Linghui Meng, Shangding Gu, Haifeng Zhang, David Mguni, Jun Wang, and Yaodong Yang · 2021
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Cmix: Deep multi-agent reinforcement learning with peak and average constraints
Chenyi Liu, Nan Geng, Vaneet Aggarwal, Tian Lan, Yuan Yang, and Mingwei Xu · 2021
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Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning
Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Başar, and Lior Horesh · 2021
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Multi-agent reinforcement learning: A selective overview of theories and algorithms
Kaiqing Zhang, Zhuoran Yang, and Tamer Başar · 2021
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Constrained reinforcement learning for vehicle motion planning with topological reachability analysis
Shangding Gu, Guang Chen, Lijun Zhang, Jing Hou, Yingbai Hu, and Alois Knoll · 2022
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The surprising effectiveness of ppo in cooperative multi-agent games
Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu · 2022
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Stackelberg actor-critic: Game-theoretic reinforcement learning algorithms
Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov, and Lillian J Ratliff · 2022
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Provably efficient generalized lagrangian policy optimization for safe multi-agent reinforcement learning
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, and Mihailo Jovanovic · 2023
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A human-centered safe robot reinforcement learning framework with interactive behaviors
Shangding Gu, Alap Kshirsagar, Yali Du, Guang Chen, Jan Peters, and Alois Knoll · 2023
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Safe multi-agent reinforcement learning for multi-robot control
Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, and Yaodong Yang · 2023
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Spatial-temporal-aware safe multi-agent reinforcement learning of connected autonomous vehicles in challenging scenarios
Zhili Zhang, Songyang Han, Jiangwei Wang, and Fei Miao · 2023
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Safe multiagent learning with soft constrained policy optimization in real robot control
Shangding Gu, Dianye Huang, Muning Wen, Guang Chen, and Alois Knoll · 2024
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