Implementation of PPO for Multi-Agent Path Finding with Dynamic Obstacles
Efficient multi-agent path finding algorithm is essential for reducing cost when deploying robots to logoistics warehouses.
In this project, we train a Multi Agent variant of Proximal Policy Optimization(PPO) algorithm for multi agent path finding with dynamic obstacles.
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Stabilizing Controllers with Root Based Polynomial Regression
We propose a novel method of stabilizing classical controllers via techniques from machine learning.
We use Polynomial Root Kernel(PRK) and Polynomial Root Gradients(PRG) to trained neural network to generate both discrete and continuous controllers satisfying root criterion stability.
We successfully generated stabilizing feed-back controllers and parallel feed-forward compensator(PFC) along with unique application to Belgian chocolate problem.
Design and Control of Scalable Magnetic Levitation System with Deep Reinforcement Learning
We Model 3DoF levitating magnetic ball with 2D plane of electro magnets on MATLAB/Simulink.
The three dimensional positional control of the levitating object was done via Deep Deterministic Policy Gradient (DDPG) algorithm.
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