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Multi-Agent Reinforcement Learning (MARL) methods find optimal policies for agents that operate in the presence of other learning agents.
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Multi-agent reinforcement learning for traffic signal control
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Learning to Communicate with Deep Multi-Agent Reinforcement Learning. In Proceedings of the 30th International Conference on Neural Information Processing Systems
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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
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Emergent Communication through Negotiation. In 6th International Conference on Learning Representations (ICLR)
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IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery; Data Mining
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Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
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Traffic signal timing via deep reinforcement learning
L. Li, Y. Lv, and F. Wang. 2016 · 2016
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Learning Multiagent Communication with Backpropagation
Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus. 2016 · 2016
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Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Serhii Havrylov and Ivan Titov. 2017 · 2017
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TarMAC: Targeted Multi-Agent Communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau. 2019 · 2019
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On Voting Strategies and Emergent Communication
Shubham Gupta and Ambedkar Dukkipati. 2019 · 2019
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On the Pitfalls of Measuring Emergent Communication. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems
Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, and Yann Dauphin. 2019 · 2019
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Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning. In 7th International Conference on Learning Representations (ICLR)
Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, and Wei Pan. 2019 · 2019
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