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

HFT & Stochastic control numerical implementations from "Optimal high frequency trading with limit and market orders" (GUILBAUD & PHAM)

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)."}

Bellman Equation, Pontryagin Maximum Principle, and Deep Learning to solve stochastic control problems

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 ``Linearizing uncertainties that matter for control: the Koopman operator in the dual control context''.

Risk-sensitive asset management simulation in Matlab.

Repository for inference method for stochastic processes through geometric path augmentation

Code for journal publication 10.1109/OJCSYS.2023.3291596

PID with neural networks

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

En este repositorio pueden encontrarse los códigos utilizados para realizar el trabajo de tesis de maestría.

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.

Linear Dynamic Systems with Unknown Parameters