A beginner-friendly reinforcement learning playground for Ludo. Train AI agents to move tokens, capture opponents, manage risk, and learn strategy through self-play. Includes a playable board interface, dice statistics, bot experiments, and educational RL examples focused on visualization and approachable learning.

A Scalable Matheuristic for Routing Capacity-Constrained Groundfish Surveys

A beginner-friendly reinforcement learning playground for Sorry!. Train AI agents to use cards, move tokens, capture opponents, and apply tactics like swaps and backward play. Includes a playable board, card analytics, bot experiments, and visual RL tools for intuitive, hands-on learning.

A collection of beginner-friendly reinforcement learning environments based on classic board games. The project focuses on making AI learning visible, interactive, and understandable through games. Includes game environments, visualization tools, statistics, bots, and educational examples for experimenting with self-play and RL strategies

A reinforcement learning environment for Mancala designed for experimentation, visualization, and education. Explore how AI agents learn resource management, planning, and tactical decision-making through self-play. Includes playable interfaces, statistics, training experiments, and beginner-focused reinforcement learning examples.

Icelandic Thesis Scraper and Comparison (Skemman + Opin vísindi)

A Python package for collecting lightweight evidence from GitHub Classroom, the underlying GitHub repositories / pull requests, and optionally Canvas.

Import/export fyrir Canvas námskeið

IDeLM‑Surgery‑Generator creates fully anonymized, realistic synthetic surgical datasets for research on intelligent scheduling, learning‑based optimization, and simulation. Inspired by real hospital patterns, it offers a public, reproducible benchmark for developing advanced decision‑support models.