no code implementations • 19 Oct 2023 • Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu, Joseph Huang
We study the implications of model uncertainty in a climate-economics framework with three types of capital: "dirty" capital that produces carbon emissions when used for production, "clean" capital that generates no emissions but is initially less productive than dirty capital, and knowledge capital that increases with R\&D investment and leads to technological innovation in green sector productivity.
no code implementations • 19 Sep 2023 • Andrea Angiuli, Jean-Pierre Fouque, Ruimeng Hu, Alan Raydan
We present the development and analysis of a reinforcement learning (RL) algorithm designed to solve continuous-space mean field game (MFG) and mean field control (MFC) problems in a unified manner.
no code implementations • 12 Jul 2023 • Robert Balkin, Hector D. Ceniceros, Ruimeng Hu
These recurrent neural network-based controls are then trained using a modified version of Brown's fictitious play, incorporating deep learning techniques.
no code implementations • 25 Apr 2023 • Ming Min, Ruimeng Hu, Tomoyuki Ichiba
Real-world data can be multimodal distributed, e. g., data describing the opinion divergence in a community, the interspike interval distribution of neurons, and the oscillators natural frequencies.
no code implementations • 17 Mar 2023 • Ruimeng Hu, Mathieu Laurière
Recently, computational methods based on machine learning have been developed for solving stochastic control problems and games.
no code implementations • 8 Oct 2022 • Chengfan Gao, Siping Gao, Ruimeng Hu, Zimu Zhu
In this paper, we provide the rigorous theory for the backward deep BSDE method.
no code implementations • 18 Aug 2022 • Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Hector D. Ceniceros
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels.
1 code implementation • 25 Apr 2022 • Jiequn Han, Ruimeng Hu, Jihao Long
These coefficient functions are used to approximate the MV-FBSDEs' model coefficients with full distribution dependence, and are updated by solving another supervising learning problem using training data simulated from the last iteration's FBSDE solutions.
no code implementations • 1 Feb 2022 • Yichen Feng, Jean-Pierre Fouque, Ruimeng Hu, Tomoyuki Ichiba
We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor.
no code implementations • 22 Jun 2021 • Jean-Pierre Fouque, Ruimeng Hu, Ronnie Sircar
The problem of portfolio optimization when stochastic factors drive returns and volatilities has been studied in previous works by the authors.
1 code implementation • 6 Jun 2021 • Ming Min, Ruimeng Hu
In this paper, based on the rough path theory, we propose a novel single-loop algorithm, named signatured deep fictitious play, by which we can work with the unfixed common noise setup to avoid the nested-loop structure and reduce the computational complexity significantly.
no code implementations • 1 Jun 2021 • Ruimeng Hu, Thaleia Zariphopoulou
In It\^{o}-diffusion environments, we introduce and analyze $N$-player and common-noise mean-field games in the context of optimal portfolio choice in a common market.
no code implementations • 24 Apr 2021 • Jiequn Han, Ruimeng Hu, Jihao Long
The proposed metrics fall into the category of integral probability metrics, for which we specify criteria of test function spaces to guarantee the property of being free of CoD.
1 code implementation • 5 Jan 2021 • Jiequn Han, Ruimeng Hu
Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions.
no code implementations • 12 Dec 2020 • Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Hector D. Ceniceros
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels.
no code implementations • 12 Aug 2020 • Jiequn Han, Ruimeng Hu, Jihao Long
Stochastic differential games have been used extensively to model agents' competitions in Finance, for instance, in P2P lending platforms from the Fintech industry, the banking system for systemic risk, and insurance markets.
no code implementations • 4 Dec 2019 • Jiequn Han, Ruimeng Hu
We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games.
no code implementations • 22 Mar 2019 • Ruimeng Hu
In this paper, we apply the idea of fictitious play to design deep neural networks (DNNs), and develop deep learning theory and algorithms for computing the Nash equilibrium of asymmetric $N$-player non-zero-sum stochastic differential games, for which we refer as \emph{deep fictitious play}, a multi-stage learning process.
no code implementations • 19 Feb 2019 • Jean-Pierre Fouque, Ruimeng Hu
This completes the analysis of portfolio optimization in both fast mean-reverting and slowly-varying Markovian stochastic environments.
no code implementations • 11 Jan 2019 • Ruimeng Hu
In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics.
no code implementations • 3 Sep 2015 • Ruimeng Hu, Mike Ludkovski
We propose and analyze sequential design methods for the problem of ranking several response surfaces.