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We examine the problem of adversarial reinforcement learning for multi-agent domains including a rule-based agent.
Markov games as a framework for multi-agent reinforcement learning
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Martin Lauer and Martin Riedmiller · 2000
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Optimal payoff functions for members of collectives
David H Wolpert and Kagan Tumer · 2002
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A flight control system for aerial robots: algorithms and experiments
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A comparison of headway and time to collision as safety indicators
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Cooperative multi-agent learning: The state of the art
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Autonomous driving in urban environments: Boss and the urban challenge
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al · 2013
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Potential-based difference rewards for multiagent reinforcement learning
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Generative adversarial nets
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Adversarial examples in the physical world
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End-to-end training of deep visuomotor policies
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Kexin Pei, Yinzhi Cao, Junfeng Yang, et al · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, et al · 2017
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Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shital Shah, Debadeepta Dey, Chris Lovett, et al · 2017
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Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, et al · 2018
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Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, et al · 2018
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Credit assignment for collective multiagent RL with global rewards
Duc Thien Nguyen, Akshat Kumar, and Hoong Chuin Lau · 2018
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Mastering the game of Go with deep neural networks and tree search
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Igor Mordatch and Pieter Abbeel · 2017
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Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray · 2018
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Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient
Shihui Li, Yi Wu, Xinyue Cui, et al · 2019
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