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Agent based modelling (ABM) is a computational approach to modelling complex systems by specifying the behaviour of autonomous decision-making components or agents in the system and allowing the system dynamics to emerge from their interactions.
ABIDES: Towards High-Fidelity Market Simulation for AI Research
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Artificial economic life: a simple model of a stockmarket
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The Swarm Simulation System:A Toolkit for Building Multi-agent Simulations
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Simulating organizations: computational models of institutions and groups
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Simulating dynamical features of escape panic
Dirk Helbing, Illés J. Farkas, and Tamás Vicsek. 2000 · 2000
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NetLogo: Design and implementation of a multi-agent modeling environment. In Proceedings of agent , Vol. 2004. Springer Cham, Switzerland, 7–9
Seth Tisue and Uri Wilensky. 2004 · 2004
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Doubleclick Ad Exchange Auction
Yishay Mansour, S. Muthukrishnan, and Noam Nisan. 2012 · 2012
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Incorporating stochastic lead times into the guaranteed service model of safety stock optimization
Salal Humair, John D Ruark, Brian Tomlin, and Sean P Willems. 2013 · 2013
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch. 2017 · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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A typology and literature review on stochastic multi-echelon inventory models
Ton de Kok, Christopher Grob, Marco Laumanns, Stefan Minner, Jörg Rambau, and Konrad Schade. 2018 · 2018
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TF-Agents: A library for Reinforcement Learning in TensorFlow
Sergio Guadarrama, Anoop Korattikara, Oscar Ramirez, Pablo Castro, Ethan Holly, Sam Fishman, Ke Wang, Ekaterina Gonina, Neal Wu, Efi Kokiopoulou, Luciano Sbaiz, Jamie Smith, Gábor Bartók, Jesse Berent, Chris Harris, Vincent Vanhoucke, and Eugene Brevdo. 2018 · 2018
Calibration of shared equilibria in general sum partially observable Markov games
Nelson Vadori, Sumitra Ganesh, Prashant Reddy, and Manuela Veloso. 2020 · 2020
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Towards a Fully RL-Based Market Simulator. In Proceedings of the Second ACM International Conference on AI in Finance (Virtual Event) (ICAIF ’21) . Association for Computing Machinery, New York, NY, USA, Article 7, 9 pages
Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann, and Sumitra Ganesh. 2021 · 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. 2021 · 2021
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Reverb: A Framework For Experience Replay
Albin Cassirer, Gabriel Barth-Maron, Eugene Brevdo, Sabela Ramos, Toby Boyd, Thibault Sottiaux, and Manuel Kroiss. 2021 · 2021
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WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
Tian Lan, Sunil Srinivasa, Huan Wang, and Stephan Zheng. 2021 · 2021
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Cited alongside, same era.
RLlib: Abstractions for Distributed Reinforcement Learning. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 3053–3062
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. 2018 · 2018
Cited alongside, same era.
Reinforcement learning for market making in a multi-agent dealer market
Sumitra Ganesh, Nelson Vadori, Mengda Xu, Hua Zheng, Prashant Reddy, and Manuela Veloso. 2019 · 2019
Cited alongside, same era.
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal M. P. Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alexander Novikov, Sergio Gomez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyun Wang, Bilal Piot, and Nando de Freitas. 2020 · 2020
Cited alongside, same era.
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Mava: a research framework for distributed multi-agent reinforcement learning
Arnu Pretorius, Kale-ab Tessera, Andries P. Smit, Claude Formanek, St John Grimbly, Kevin Eloff, Siphelele Danisa, Lawrence Francis, Jonathan Shock, Herman Kamper, Willie Brink, Herman Engelbrecht, Alexandre Laterre, and Karim Beguir. 2021 · 2021
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Stable-Baselines3: Reliable Reinforcement Learning Implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. 2021 · 2021
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Launchpad: A Programming Model for Distributed Machine Learning Research
Fan Yang, Gabriel Barth-Maron, Piotr Stańczyk, Matthew Hoffman, Siqi Liu, Manuel Kroiss, Aedan Pope, and Alban Rrustemi. 2021 · 2021
Later among the works it cites.