  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 2022. Unlike
previous agent-based models (ABM) or multi-agent systems (MAS) which relied on
zero-intelligence agents or single autonomous agent methodologies, our approach
relies on endowing agents with reinforcement learning (RL) techniques in order
to model crypto markets. This integration is designed to emulate, with a
bottom-up approach to complexity inference, both individual and collective
agents, ensuring robustness in the recent volatile conditions of such markets
and during the COVID-19 era. A key feature of our model also lies in the fact
that its autonomous agents perform asset price valuation based on two sources
of information: the market prices themselves, and the approximation of the
crypto assets fundamental values beyond what those market prices are. Our MAS
calibration against real market data allows for an accurate emulation of crypto
markets microstructure and probing key market behaviors, in both the bearish
and bullish regimes of that particular time period.
