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Found 21 papers, 3 papers with code
A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
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.
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces
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.
Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms
These recurrent neural network-based controls are then trained using a modified version of Brown's fictitious play, incorporating deep learning techniques.
Directed Chain Generative Adversarial Networks
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.
Recent Developments in Machine Learning Methods for Stochastic Control and Games
Recently, computational methods based on machine learning have been developed for solving stochastic control problems and games.
Convergence of the Backward Deep BSDE Method with Applications to Optimal Stopping Problems
In this paper, we provide the rigorous theory for the backward deep BSDE method.
Pandemic Control, Game Theory and Machine Learning
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels.
Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence
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.
Systemic Risk Models for Disjoint and Overlapping Groups with Equilibrium Strategies
We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor.
Sub- and Super-solution Approach to Accuracy Analysis of Portfolio Optimization Asymptotics in Multiscale Stochastic Factor Market
The problem of portfolio optimization when stochastic factors drive returns and volatilities has been studied in previous works by the authors.
Signatured Deep Fictitious Play for Mean Field Games with Common Noise
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.
$N$-player and Mean-field Games in Itô-diffusion Markets with Competitive or Homophilous Interaction
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.
A Class of Dimension-free Metrics for the Convergence of Empirical Measures
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.
Recurrent Neural Networks for Stochastic Control Problems with Delay
Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions.
Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels.
Convergence of Deep Fictitious Play for Stochastic Differential Games
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.
Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games
We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games.
Deep Fictitious Play for Stochastic Differential Games
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.
Multiscale Asymptotic Analysis for Portfolio Optimization under Stochastic Environment
This completes the analysis of portfolio optimization in both fast mean-reverting and slowly-varying Markovian stochastic environments.
Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems
In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics.
Sequential Design for Ranking Response Surfaces
We propose and analyze sequential design methods for the problem of ranking several response surfaces.
