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Deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators recently.
Agent-based simulation of a financial market
Marco Raberto, Silvano Cincotti, Sergio M Focardi, and Michele Marchesi · 2001
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A brief review of recent artificial market simulation (agent-based model) studies for financial market regulations and/or rules
Takanobu Mizuta · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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RLlib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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DataOps: An agile methodology for data-driven organizations
Crystal Valentine and William Merchan · 2018
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Multi-agent deep reinforcement learning for liquidation strategy analysis, 2019
Wenhang Bao and Xiao yang Liu · 2019
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Stable baselines3
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Open-ended learning leads to generally capable agents
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Embodied intelligence via learning and evolution
Agrim Gupta, Silvio Savarese, Surya Ganguli, and Li Fei-Fei · 2021
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ElegantRL-Podracer: Scalable and elastic library for cloud-native deep reinforcement learning
Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, and Michael Jordan · 2021
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FinRL: Deep reinforcement learning framework to automate trading in quantitative finance
Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, and Christina Dan Wang · 2021
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Modelling stock markets by multi-agent reinforcement learning
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Deep reinforcement learning in quantitative algorithmic trading: A review
Tidor-Vlad Pricope · 2021
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