Modelling crypto markets by multi-agent reinforcement learning

Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 20...

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Hauptverfasser:	Lussange, Johann, Vrizzi, Stefano, Palminteri, Stefano, Gutkin, Boris
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Computer Science and Game Theory Computer Science - Multiagent Systems Quantitative Finance - Computational Finance
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