Repository for inference method for stochastic processes through geometric path augmentation
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Updated
May 25, 2023 - Python
Repository for inference method for stochastic processes through geometric path augmentation
Code for journal publication 10.1109/OJCSYS.2023.3291596
The programs written over the summer of 2021 while working for the University of Delaware's Information and Decision Sciences (IDS) Lab. For more information about the lab and its other projects, please visit https://sites.udel.edu/ids-lab/ . This repository and README will be updated somewhat reguarly as progress is made on these projects.
Risk-sensitive asset management simulation in Matlab.
En este repositorio pueden encontrarse los códigos utilizados para realizar el trabajo de tesis de maestría.
This code can be used to reproduce the results in our paper ``Linearizing uncertainties that matter for control: the Koopman operator in the dual control context''.
Control + Bellman equation to find arbitrage strategies in Constant Product Market with constant fees
This code can be used to reproduce the results in our paper ``A Control Approach for Nonlinear Stochastic State Uncertain Systems with Probabilistic Safety Guarantees''.
The goal of this project is to explore and compare the different methods of solving optimal control problems in dynamical systems
Linear Dynamic Systems with Unknown Parameters
Bellman Equation, Pontryagin Maximum Principle, and Deep Learning to solve stochastic control problems
Paper: Challenges in High-dimensional Reinforcement Learning with Evolution Strategies
Code for the paper {Pang, Bo, and Zhong-Ping Jiang. "Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems." arXiv preprint arXiv:2107.07788 (2021)."}
HFT & Stochastic control numerical implementations from "Optimal high frequency trading with limit and market orders" (GUILBAUD & PHAM)
This repository contains classwork and practice examples based on Model Predictive Control. Robust and Stochastic control methods applied to and studied for linear/non-linear plants.
Code for thesis project on applying reinforcement learning to algorithmic trading
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