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In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal.
Dealing with non-stationarity in multi-agent deep reinforcement learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, and Stefano V. Albrecht · 1906
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Smoothing and Differentiation of Data by Simplified Least Squares Procedures
Abraham. Savitzky and M. J. E. Golay · 1964
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Investigating causal relations by econometric models and cross-spectral methods
C. W. J. Granger · 1969
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Technical Note Q,-Learning
Christopher Watkins and Peter Dayan · 1992
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Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents
Ming Tan · 1993
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Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Sindy Löwe, David Madras, Richard Zemel, and Max Welling · 2006
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Causal discovery from incomplete data using an encoder and reinforcement learning, 2020
Xiaoshui Huang, Fujin Zhu, Lois Holloway, and Ali Haidar · 2006
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Reward Machines for Cooperative Multi-Agent Reinforcement Learning
Cyrus Neary, Zhe Xu, Bo Wu, and Ufuk Topcu · 2007
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QPLEX: Duplex Dueling Multi-Agent Q-Learning
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang · 2008
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Optimal and approximate q-value functions for decentralized pomdps
Frans A. Oliehoek, Matthijs T. J. Spaan, and Nikos Vlassis · 2008
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Granger Causality: Theory and Applications , pages 83–111
Shuixia Guo, Christophe Ladroue, and Jianfeng Feng · 2010
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Granger causality analysis in neuroscience and neuroimaging
Anil K. Seth, Adam B. Barrett, and Lionel Barnett · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis, and Amir Sadik · 2015
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Multiagent Cooperation and Competition with Deep Reinforcement Learning
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, and Raul Vicente · 2015
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Multi-agent reinforcement learning as a rehearsal for decentralized planning
Landon Kraemer and Bikramjit Banerjee · 2016
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Learning Multiagent Communication with Backpropagation
Sainbayar Sukhbaatar, arthur szlam, and Rob Fergus · 2016
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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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A Concise Introduction to Decentralized POMDPs
Frans Oliehoek A. and Christopher Amato · 2016
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Cooperative Multi-agent Control Using Deep Reinforcement Learning
Jayesh K. Gupta, Maxim Egorov, and Mykel Kochenderfer · 2017
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Elements of Causal Inference
Jonas Peters, Dominik Janzing, and Bernhard Scholkopf · 2017
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QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Hostallero, and Yung Yi · 2019
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Liir: Learning individual intrinsic reward in multi-agent reinforcement learning
Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, and Dacheng Tao · 2019
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Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
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The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philiph H. S. Torr, Jakob Foerster, and Shimon Whiteson · 2019
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Multi-Agent Common Knowledge Reinforcement Learning
Christian A. Schroeder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, and Shimon Whiteson · 2020
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Reward shaping in episodic reinforcement learning
Marek Grzeundefined · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Value-Decomposition Networks For Cooperative Multi-Agent Learning
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, and Thore Graepel · 2018
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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
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Counterfactual Multi-Agent Policy Gradients
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Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Shapley q-value: A local reward approach to solve global reward games
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Economy statistical recurrent units for inferring nonlinear granger causality
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Causal discovery with reinforcement learning
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Bottom-up multi-agent reinforcement learning by reward shaping for cooperative-competitive tasks
Takumi Aotani, Taisuke Kobayashi, and Kenji Sugimoto · 2021
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Scaling multi-agent reinforcement learning with selective parameter sharing
Filippos Christianos, Georgios Papoudakis, Muhammad A Rahman, and Stefano V Albrecht · 2021
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Neural granger causality
Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, and Emily B Fox · 2021
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Causal multi-agent reinforcement learning: Review and open problems
St John Grimbly, Jonathan P. Shock, and Arnu Pretorius · 2021
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Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Daniele Giardino, Marco Re, and Sergio Spanò · 2076
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