7 citations · 16 across the 9 of their papers we have counts for
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q-fin.CP2024★ 1 cited
Modelling crypto markets by multi-agent reinforcement learning
Johann Lussange, Stefano Vrizzi, Stefano Palminteri +1
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 calibrate…
cs.AI2024
Continuous Time Continuous Space Homeostatic Reinforcement Learning (CTCS-HRRL) : Towards Biological Self-Autonomous Agent
Hugo Laurencon, Yesoda Bhargava, Riddhi Zantye +4
Homeostasis is a biological process by which living beings maintain their internal balance. Previous research suggests that homeostasis is a learned behaviour. Recently introduced…