RocketMeister is an extensive and sophisticated gym environment for developing and comparing reinforced learning algorithms.

Inteligencia artificial que utiliza aprendizaje reforzado para aprender a jugar al minecraft en tiempo real. Todavía estoy trabajando en ello!

Made for the Covid India Gameathon

A computer vision pipeline built with PyTorch for advanced NBA analytics. The system uses YOLO for player/ball detection, multi-object tracking, and a zero-shot classifier for team affiliation, and court key point detection to create a tactical top-down view and calculate real-world metrics like speed, distance, and passes.

An Ai using Reinforcement Learning to survive in a very basic roguelike environment

Deep Q learning applied to snake's game

Fully functional group dating site works based on mutual priority. (to create more fair and less exhaustive experience than Tinder)

Solutions for IEEE Hackathon 2023 challenges, featuring space mission success analysis with ML and simulations, and reinforcement learning-based automata theory demonstrations.

RLAC is a AI based chatbot that at its core uses basic reinforced learning with the Epsilon-Greedy Policy

Reinforced Learning algorithm to teach a car how to go around a track

Machine Learning Workshop notes

intelligent, multi-strategy trading system for MetaTrader 5 that uses XGBoost and PyTorch models for price prediction, emotion-based risk management, and automated profit-taking. It pairs a FastAPI backend with a React/Vite dashboard for real-time monitoring and is intended for research/demo use

This is a fork from my capstone project for the CSU Monterey Bay bachelor's of computer science degree program.

Online Decision Transformer

To develop a dynamic pricing optimization system that leverages customer review sentiment analysis to inform pricing strategies. The project aimed to analyze Amazon Beauty product reviews, extract sentiment insights using NLP techniques, and use these insights to make data-driven pricing recommendations.

An Reinforcement-Learning Agent for autonomous server autoscaling, trained with DQN(Deep Q-Network) in a custom Gymnasium environment.

This repository proposes a tutorial on reinforced learning for beginners where the main concepts of this type of learning are introduced in a straightforward and applied way. A Python application of the Q-learning algorithm is implemented to solve a "maze-world" problem.

A reinforcement learning environment implementing Q-learning where an agent navigates a 10x10 grid to collect food while avoiding enemies. Features epsilon-greedy exploration, Q-table state-action optimization, and real-time PyGame/OpenCV visualization of agent movement and learning progress.