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Most real-world domains can be formulated as multi-agent (MA) systems.
Emanuele Pesce and Giovanni Montana · 1901
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Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles
Fatemeh Afghah, Abolfazl Razi, Jacob Chakareski, and Jonathan Ashdown · 1905
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Optimal algebraic Breadth-First Search for sparse graphs
Paul Burkhardt · 1906
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Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 1909
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Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
Paul Almasan, José Suárez-Varela, Arnau Badia-Sampera, Krzysztof Rusek, Pere Barlet-Ros, and Albert Cabellos-Aparicio · 1910
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Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Kaiqing Zhang, Zhuoran Yang, and Tamer Başar · 1911
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Diplomacy (game), 1959
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On the origin of species by means of natural selection, or, The preservation of favoured races in the struggle for life., 1977
Charles Darwin · 1977
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Infanticide in Prairie Dogs: Lactating Females Kill Offspring of Close Kin
John L. Hoogland · 1985
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An image synthesizer
Ken Perlin · 1985
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Distributed problem-solving techniques: A survey
Keith S. Decker · 1987
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On Team Formation
Philip Cohen, Hector Levesque, and Ira Smith · 1997
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Using communication to reduce locality in distributed multiagent learning
MAJA J. MATARIC · 1998
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Swarm Intelligence: From Natural to Artificial Systems
Eric Bonabeau, Marco Dorigo, and Guy Theraulaz · 1999
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An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
Martin Lauer and Martin Riedmiller · 2000
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Learning to Cooperate via Policy Search, 2000
Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau, and Leslie Pack Kaelnling · 2000
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Communication decisions in multi-agent cooperation: model and experiments
Ping Xuan, Victor Lesser, and Shlomo Zilberstein · 2001
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Deep Blue
Murray Campbell, A. Joseph Hoane, and Feng-hsiung Hsu · 2002
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Breeding Together: Kin Selection and Mutualism in Cooperative Vertebrates
Tim Clutton-Brock · 2002
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Coordinated Reinforcement Learning
Carlos Guestrin, Michail Lagoudakis, and Ronald Parr · 2002
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Communication efficiency in multi-agent systems
M. Berna-Koes, I. Nourbakhsh, and K. Sycara · 2004
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Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems
William J. Bond and Jon E. Keeley · 2005
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Cooperative Multi-Agent Learning: The State of the Art
Liviu Panait and Sean Luke · 2005
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Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Roman Werpachowski, Satinder Singh, Thore Graepel, and Yoram Bachrach · 2006
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The NetHack Learning Environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, and Tim Rocktäschel · 2006
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Using Unity to Help Solve Intelligence
Tom Ward, Andrew Bolt, Nik Hemmings, Simon Carter, Manuel Sanchez, Ricardo Barreira, Seb Noury, Keith Anderson, Jay Lemmon, Jonathan Coe, Piotr Trochim, Tom Handley, and Adrian Bolton · 2011
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Analysis of the Depth First Search Algorithms
N. Kaur and D. Garg · 2012
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Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems
Laëtitia Matignon, Guillaume J. Laurent, and Nadine Le Fort-Piat · 2012
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Decentralized POMDPs
Frans A. Oliehoek · 2012
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Reinforcement Learning in Robotics: A Survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
Noam Brown and Tuomas Sandholm · 2018
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Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images
Sriram Ganapathi Subramanian and Mark Crowley · 2018
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Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots
Ravi N. Haksar and Mac Schwager · 2018
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A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions
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Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Industrial Agents: Emerging Applications of Software Agents in Industry
Paulo Leitão and Stamatis Karnouskos · 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, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 2015
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Animal Communication and Human Language: An overview
Leonardo Barón Birchenall · 2016
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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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Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
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Zool Hilmi Ismail and Nohaidda Sariff · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Dota 2 with Large Scale Deep Reinforcement Learning
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Scientists’ warning on wildfire — a Canadian perspective
Sean C.P. Coogan, François-Nicolas Robinne, Piyush Jain, and Mike D. Flannigan · 2019
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A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, and Enrique Munoz de Cote · 2019
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Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction
Kyle D. Julian and Mykel J. Kochenderfer · 2019
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An Introduction to Proximal Policy Optimization (PPO) in Deep Reinforcement Learning, April 2019
Udacity-DeepRL · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander S. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
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The Complete Reinforcement Learning Dictionary, November 2019
Shaked Zychlinski · 2019
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LoRa and LoRaWAN: Technical overview | DEVELOPER PORTAL, February 2020
Semtech Corporation · 2020
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Climate change is increasing the likelihood of extreme autumn wildfire conditions across California
Michael Goss, Daniel L. Swain, John T. Abatzoglou, Ali Sarhadi, Crystal A. Kolden, A. Park Williams, and Noah S. Diffenbaugh · 2020
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A review of machine learning applications in wildfire science and management
Piyush Jain, Sean C.P. Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, and Mike D. Flannigan · 2020
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Machine Learning Wildfire Prediction based on Climate Data
Yujian Xiong, Jie Wu, and Zizhan Chen · 2020
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On the Importance of Environments in Human-Robot Coordination
Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, and Stefanos Nikolaidis · 2021
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Fantine Huot, R. Lily Hu, Nita Goyal, Tharun Sankar, Matthias Ihme, and Yi-Fan Chen · 2021
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Open-Ended learning leads to Generally Capable Agents
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Proximal Policy Optimization — Spinning Up documentation, 2021
Spinning Up OpenAI · 2021
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The Power of Communication in a Distributed Multi-Agent System
Philipp Dominic Siedler · 2021
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2021
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DeepMind: The Podcast - Better together, January 2022
Hannah Fry · 2022
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