This research compared three reinforcement learning (RL) algorithms (SAC, PPO, DDPG) to traditional PID control for water level control in single-tank and quadruple-tank systems. The RL algorithms were trained using MATLAB's Reinforcement Learning Toolbox and tested in a Simulink simulation.

This is a MATLAB-based reinforcement learning framework that includes the Proximal Policy Optimization (PPO) algorithm and its multi-agent extension (MAPPO). It supports GPU acceleration and parallel computing, making it suitable for research and engineering applications in control systems.

Reinforcement Learning WaterTank Control on MATLAB with Custom Agent, the agent instead of DDPG, we used PPO

Used PPO Agent to target exploration for Obstacle Avoidance using MATLAB

MATLAB实现的PPO-LSSVM，然后在C++中调用

A project integrating Machine Learning (RF classification + PPO RL) and MATLAB-based end-to-end communication simulation to build an intelligent anti-jamming MIMO system.

This repository contains MATLAB code (PPO Algorithm) and datasets used in the paper: "Development of Novel Reinforcement Learning-Based Optimizer to Impede Tumor Growth via Radiochemotherapy"

This project trains a Proximal Policy Optimization (PPO) agent to control yaw angles in a 9‑turbine wind farm using the WFSim MATLAB simulator. It provides a Python reinforcement learning workflow that interfaces with MATLAB via the Engine API to optimize wind farm power output.

Matlab_PPO_GCN_Proximal_Policy_Optimization_Graph_Convolutional_Networks

Repo dump of my PPO drone project in matlab

MATLAB examples that use Proximal Policy Optimization (PPO) to calibrate integrated photonic devices

This repository implements a reinforcement learning–based torque controller for EV motor drives. Python RL agents (PPO via Stable-Baselines3) are integrated with a high-fidelity MATLAB/Simulink model using the MATLAB Engine API to optimize torque control for improved energy efficiency, reduced torque ripple, and minimal speed overshoot.

An autonomous agent for pavement structure design integrating Large Language Models (LLMs) and Proximal Policy Optimization (PPO). Features physics-informed FEA constraints and supports both proprietary (GPT-4) and open-source models (Llama-3/Qwen) via MATLAB.

Implemented Proximal Policy Optimization (PPO) in Reinforcement Learning using MATLAB to enable stable and efficient movement of a quadruped robot on complex terrains. The approach improves the robot’s control by balancing learning and performance, enhancing stability and adaptability for applications like goods transportation and surveillance.