Q-Learning algorithm for Particle Swarm Optimization
Multi-agent optimization using NetLogo environment
Our developers have implemented the code for simulation of the multi-agent optimization process in the NetLogo environment.
Algorithm is an enhanced version of the particle swarm optimization method where in addition to selection of the appropriate movement velocity in the problem search space, each agent also learns an optimal strategy for internal parameter selection based on his current position.
These parameters include maximum and minimum velocity values, the size of the neighborhood taken into account, trade-off between the effect of global optimum found so far and optimum value in the neighborhood etc.
The algorithm was implemented based on the description in the paper “Intelligent Particle Swarm Optimization Using Q-Learning” by M. Khajenejad, F. Afshinmanesh, A. Mar and B. Araabi. For all the required parameters, the corresponding graphical interface controls was prepared. Code was commented and documented.
Virtual try-on tool for makeup products
The system consists of a face detection and segmentation model and an algorithm that allows recoloring objects without losing their original texture.
Online sign language interpreter
AI algorithm that converts video of a person using sign language into a text transcript
Workout helper app
Mobile app for the estimation of proper body positions during the workout.