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Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces.
An overview of recent progress in the study of distributed multi-agent coordination
Yongcan Cao, Wenwu Yu, Wei Ren, and Guanrong Chen · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
A multi-agent framework for packet routing in wireless sensor networks
Dayong Ye, Minjie Zhang, and Yun Yang · 2015
Earlier work this paper cites.
Learning to communicate with deep multi-agent reinforcement learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson · 2016
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Deep reinforcement learning in parameterized action space
Matthew J. Hausknecht and Peter Stone · 2016
Earlier work this paper cites.
Half field offense : An environment for multiagent learning and ad hoc teamwork
Matthew J. Hausknecht · 2016
Cited alongside, same era.
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum · 2016
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Cited alongside, same era.
Reinforcement learning with parameterized actions
Warwick Masson, Pravesh Ranchod, and George Konidaris · 2016
Cited alongside, same era.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
Cited alongside, same era.
Multiagent bidirectionally-coordinated nets for learning to play starcraft combat games
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, and Shimon Whiteson · 2018
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Hierarchical deep multiagent reinforcement learning
Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Changjie Fan, and Li Wang · 2018
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Exponentially weighted imitation learning for batched historical data
Qing Wang, Jiechao Xiong, Lei Han, Peng Sun, Han Liu, and Tong Zhang · 2018
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Multiagent soft q-learning
Ermo Wei, Drew Wicke, David Freelan, and Sean Luke · 2018
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Hierarchical approaches for reinforcement learning in parameterized action space
Ermo Wei, Drew Wicke, and Sean Luke · 2018
Later among the works it cites.
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Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, and Jun Wang · 2017
Cited alongside, same era.
Jiechao Xiong, Qing Wang, Zhuoran Yang, Peter P Sun, Lei Han, Yang Zheng, Haobo Fu, Tong Zhang, Ji Liu, and Hao Liu · 2018
Later among the works it cites.