Research Intern @ Retail Realm • McGill B.Sc. Graduate
Hi, I'm Jonathan.
Programmer, chess player & curious mind.
Research Intern at Retail Realm and McGill Computer Science graduate specializing in Reinforcement Learning, Machine Learning algorithms, and intelligent optimization for Mining and complex systems.
Focus:
Reinforcement Learning
Machine Learning
Computer Science
Mining & Industrial RL
Python / PyTorch
Research Intern @ Retail Realm
B.Sc. CS • McGillFeatured Projects
Selected Works
A showcase of recent software engineering projects, web applications, and technical experiments.
Machine Learning & RL
Featured
Modular RL (Functional Core for PyTorch)
High-performance researcher-centric Reinforcement Learning library for PyTorch built on the Functional Core, Imperative Shell design pattern (JAX/RLax style architecture).
Machine Learning & RL
Systems Engineering
PyTorch
Python
Reinforcement Learning
Software Architecture
Machine Learning & RL
Featured
VectorHaSH: Bio-Inspired Grid Cell Navigation Scaffold
Computational neuroscience and spatial navigation model combining Continuous Attractor Networks (CAN) and hippocampal grid cell scaffolds for spatial localization in AnimalAI environments.
Machine Learning & RL
Simulations
PyTorch
Python
Neuroscience AI
Continuous Attractor Networks
Machine Learning & RL
From DQN to Rainbow: Deep Q-Learning Suite
A modular PyTorch suite building incrementally from vanilla Deep Q-Networks (DQN) to full Rainbow DQN (Double DQN, Dueling, PER, Noisy Nets, n-Step, C51) and NFSP.
Machine Learning & RL
PyTorch
Python
Deep Q-Learning
Rainbow DQN
Reinforcement Learning
Want to see more projects?
Explore the full portfolio archive including open-source libraries, machine learning experiments, and utilities.
View Full PortfolioAbout & Background
Reinforcement Learning & Mining Optimization
Combining a Computer Science degree from McGill University with advanced Reinforcement Learning, Machine Learning algorithms, and Mining operational optimization.
McGill Computer Science
Bachelor of Science in Computer Science from McGill University, focusing on theoretical algorithms, data structures, and computational optimization.
Reinforcement Learning & ML
Researching deep reinforcement learning (PyTorch, PettingZoo, Gym), multi-agent policies, and machine learning models for complex decision environments.
Mining & Industrial RL
Applying RL policies and intelligent dispatch algorithms to solve real-world mining problems like Long-Haul Truck (LHT) switch optimization.
Get In Touch
Let's Work Together
Have a project in mind, a question, or a software engineering opportunity? Send a message below.
Send a Message
Fill out the form below or reach out directly.
Direct Email: jonathan.lamontagne.kratz@gmail.com
Phone: +1 (613) 697-0240