7 citations · 16 across the 9 of their papers we have counts for
9 papers
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…
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…
3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction
Johann Lussange, Mulin Yu, Yuliya Tarabalka +1
Reconstructing urban areas in 3D out of satellite raster images has been a long-standing and challenging goal of both academical and industrial research. The rare methods today ach…
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective
Johann Lussange, Boris Gutkin
Recent technological developments have changed the fundamental ways stock markets function, bringing regulatory instances to assess the benefits of these developments. In parallel,…
Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents
Hugo Laurençon, Charbel-Raphaël Ségerie, Johann Lussange +1
Homeostasis is a prevalent process by which living beings maintain their internal milieu around optimal levels. Multiple lines of evidence suggest that living beings learn to act t…
Stock market microstructure inference via multi-agent reinforcement learning
J. Lussange, I. Lazarevich, S. Bourgeois-Gironde +2
Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling age…
Mesoscale impact of trader psychology on stock markets: a multi-agent AI approach
J. Lussange, S. Palminteri, S. Bourgeois-Gironde +1
Recent advances in the fields of machine learning and neurofinance have yielded new exciting research perspectives in practical inference of behavioural economy in financial market…
Stock price formation: useful insights from a multi-agent reinforcement learning model
J. Lussange, S. Bourgeois-Gironde, S. Palminteri +1
In the past, financial stock markets have been studied with previous generations of multi-agent systems (MAS) that relied on zero-intelligence agents, and often the necessity to im…
A bright future for financial agent-based models
J. Lussange, A. Belianin, S. Bourgeois-Gironde +1
The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natu…