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Sample efficiency is a critical challenge in reinforcement learning.
On a test of whether one of two random variables is stochastically larger than the other
Henry B Mann and Donald R Whitney · 1947
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Stochastic games
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Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment
Auke Jan Ijspeert, Alcherio Martinoli, Aude Billard, and Luca Maria Gambardella · 2001
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The complexity of decentralized control of markov decision processes
Daniel S Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein · 2002
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Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, and Stefano V. Albrecht · 2006
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The binocular advantage in visuomotor tasks involving tools
Jenny C. A. Read, Shah Farzana Begum, Alice McDonald, and Jack Trowbridge · 2013
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
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Overcooked, 2016
Ghost Town Games · 2016
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Learning multiagent communication with backpropagation
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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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Revisiting the master-slave architecture in multi-agent deep reinforcement learning
Xiangyu Kong, Bo Xin, Fangchen Liu, and Yizhou Wang · 2017
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Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, and Jun Wang · 2017
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Learning attentional communication for multi-agent cooperation
Jiechuan Jiang and Zongqing Lu · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-ming Wu · 2018
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Traffic signal control based on reinforcement learning with graph convolutional neural nets
Tomoki Nishi, Keisuke Otaki, Keiichiro Hayakawa, and Takayoshi Yoshimura · 2018
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Graph neural networks for learning robot team coordination
Amanda Prorok · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Deep dominance-how to properly compare deep neural models
Rotem Dror, Segev Shlomov, and Roi Reichart · 2019
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Quantifying the carbon emissions of machine learning
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres · 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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Prefixrl: Optimization of parallel prefix circuits using deep reinforcement learning
Rajarshi Roy, Jonathan Raiman, Neel Kant, Ilyas Elkin, Robert Kirby, Michael Siu, Stuart Oberman, Saad Godil, and Bryan Catanzaro · 2021
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Mambpo: Sample-efficient multi-robot reinforcement learning using learned world models
Daniël Willemsen, Mario Coppola, and Guido CHE de Croon · 2021
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Mastering atari games with limited data
Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao · 2021
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Model-based multi-agent policy optimization with adaptive opponent-wise rollouts
Weinan Zhang, Xihuai Wang, Jian Shen, and Ming Zhou · 2021
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John P Agapiou, Alexander Sasha Vezhnevets, Edgar A Duéñez-Guzmán, Jayd Matyas, Yiran Mao, Peter Sunehag, Raphael Köster, Udari Madhushani, Kavya Kopparapu, Ramona Comanescu, et al · 2022
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Google research football: A novel reinforcement learning environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, et al · 2020
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Graph neural networks for decentralized multi-robot path planning
Qingbiao Li, Fernando Gama, Alejandro Ribeiro, and Amanda Prorok · 2020
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Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver · 2020
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Learning decentralized controllers for robot swarms with graph neural networks
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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Vmas: A vectorized multi-agent simulator for collective robot learning
Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp, and Amanda Prorok · 2022
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2022
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Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean-Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis, and Martin Riedmiller · 2022
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Scalable multi-agent model-based reinforcement learning
Vladimir Egorov and Aleksei Shpilman · 2022
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Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning, 2022
Benjamin Ellis, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Mahajan, Jakob N. Foerster, and Shimon Whiteson · 2022
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Towards a standardised performance evaluation protocol for cooperative marl
Rihab Gorsane, Omayma Mahjoub, Ruan John de Kock, Roland Dubb, Siddarth Singh, and Arnu Pretorius · 2022
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Qgnn: Value function factorisation with graph neural networks
Ryan Kortvelesy and Amanda Prorok · 2022
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Controlling commercial cooling systems using reinforcement learning
Jerry Luo, Cosmin Paduraru, Octavian Voicu, Yuri Chervonyi, Scott Munns, Jerry Li, Crystal Qian, Praneet Dutta, Jared Quincy Davis, Ningjia Wu, et al · 2022
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The primacy bias in deep reinforcement learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, and Aaron Courville · 2022
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Heterogeneous multi-robot reinforcement learning
Matteo Bettini, Ajay Shankar, and Amanda Prorok · 2023
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Mastering diverse domains through world models, 2023
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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