An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arXiv preprint arXiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Learning mean-field games
This paper presents a general mean-field game (GMFG) framework for simultaneous
learning and decision-making in stochastic games with a large population. It first establishes …
learning and decision-making in stochastic games with a large population. It first establishes …
Reinforcement learning and stochastic optimisation
S Jaimungal - Finance and Stochastics, 2022 - Springer
At the heart of financial mathematics lie stochastic optimisation problems. Traditional
approaches to solving such problems, while applicable to broad classes of models, require …
approaches to solving such problems, while applicable to broad classes of models, require …
A general framework for learning mean-field games
This paper presents a general mean-field game (GMFG) framework for simultaneous
learning and decision making in stochastic games with a large population. It first establishes …
learning and decision making in stochastic games with a large population. It first establishes …
A mean‐field game approach to equilibrium pricing in solar renewable energy certificate markets
Solar renewable energy certificate (SREC) markets are a market‐based system that
incentivizes solar energy generation. A regulatory body overseeing load serving entities …
incentivizes solar energy generation. A regulatory body overseeing load serving entities …
A mean field game inverse problem
Mean-field games arise in various fields, including economics, engineering, and machine
learning. They study strategic decision-making in large populations where the individuals …
learning. They study strategic decision-making in large populations where the individuals …
A mean field game of optimal portfolio liquidation
G Fu, P Graewe, U Horst… - Mathematics of Operations …, 2021 - pubsonline.informs.org
We consider a mean field game (MFG) of optimal portfolio liquidation under asymmetric
information. We prove that the solution to the MFG can be characterized in terms of a forward …
information. We prove that the solution to the MFG can be characterized in terms of a forward …
A mean field game approach to equilibrium pricing with market clearing condition
M Fujii, A Takahashi - SIAM Journal on Control and Optimization, 2022 - SIAM
In this work, we study an equilibrium-based continuous asset pricing problem which seeks to
form a price process endogenously by requiring it to balance the flow of sale-and-purchase …
form a price process endogenously by requiring it to balance the flow of sale-and-purchase …
Deep learning for principal-agent mean field games
S Campbell, Y Chen, A Shrivats… - arXiv preprint arXiv …, 2021 - arxiv.org
Here, we develop a deep learning algorithm for solving Principal-Agent (PA) mean field
games with market-clearing conditions--a class of problems that have thus far not been …
games with market-clearing conditions--a class of problems that have thus far not been …
How to build a cross-impact model from first principles: Theoretical requirements and empirical results
Full article: How to build a cross-impact model from first principles: theoretical requirements
and empirical results Skip to Main Content Taylor and Francis Online homepage Taylor and …
and empirical results Skip to Main Content Taylor and Francis Online homepage Taylor and …