Edward Feng's starred repositories
efficient-kan
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
StableNODE
Stable Neural Differential Equations
MATLABAutoDiff
Automatic Differentiation Package for MATLAB
Convolutional-Neural-Network
This is a matlab implementation of CNN on MNIST
Matlab-toolbox-for-DNN-based-speech-separation
This folder contains Matlab programs for a toolbox for supervised speech separation using deep neural networks (DNNs).
Deep_NeuralNetwork_CNN_MATLAB
This repository contains various deep CNN and NN architectures coded in MATLAB. This is also a demonstration of ease of using MATLAB for network design and implementation.
TNNLS-2022--CT-RL-Optimal-Control
Contains all research-related code for publications by Brent Wallace, Arizona State University
ControleRoboManipulador
Reinforcement Learning Control of KUKA-KR16 Industrial Robot in Matlab
PPO-LSTM-deep-reinforcement-learning-based-controller-for-buck-boost-converter-with-constant-power-l
PPO-LSTM deep reinforcement learning based controller for buck-boost converter with constant power load Data File
Reinforcement_Learning_Control
Training a Reinforcement Learning agent to tune traditional controllers.
Structural-Scheduling-of-Transient-Control-System
These are source code files of simulation for paper structural scheduling of transient control system under enery storage systems by sparse promote reinforcement learning
Function-Approximation-and-Adaptive-PID-Gain-Tuning-using-Neural-Networks-and-Reinforcement-Learning
System Identification and Self-Tuning PID Control using NN
rl-agent-based-traffic-control
Develop agent-based traffic management system by model-free reinforcement learning
Implicit-Deep-Learning
Implementation of Implicit Deep Learning
Deep-Reinforcement-Learning-Algorithms
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
simulink_python
使用simulink进行环境的模拟，使用python编写强化学习(rl)代码
Cycle-based-Battery-Controller
This folder contains the codes and data used for papers: Shi, Yuanyuan, Bolun Xu, Yushi Tan, Daniel Kirschen, and Baosen Zhang. "Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings." IEEE Transactions on Automatic Control.
DRL-for-microgrid-energy-management
We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.
Energy_Systems_and_Control-Projects
Projects related to Energy Systems and Control covering Flight Path Optimization, Battery Modeling, State Estimation, Optimal Economic Dispatch of Distribution, Forecasting Electricity Power Consumption, Optimal PHEV Energy Management
pscc2020-load-limiting
Computational experiment code for Lee et al submission to PSCC 2020 title "Non-Intrusive Load Management Under Forecast Uncertainty in Energy Constrained Microgrids"
microgrid-dispatch-simulator
Models and simulation loops for energy management and power and load dispatch in community microgrids with distributed energy
joint-optimization
This folder contains the codes and data used for papers: Shi, Yuanyuan, Bolun Xu, Di Wang, and Baosen Zhang. "Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains." IEEE Transactions on Power System.