Pirate-AI trains a treasure-seeking Q-learning agent

Pirate-AI is a Jupyter Notebook project that trains a pirate agent with deep Q-learning to reach treasure.

Pirate-AI is a tiny but instructive reinforcement learning project: one GitHub star, zero forks, and a notebook-based implementation focused on path finding. The goal is simple to state and hard to make work well in code, which is why this repo is interesting.

What this project is trying to do

The repository frames the problem as a pirate trying to reach treasure by learning which actions produce the best outcome over time. Instead of hard-coding a route, the agent learns from reward signals, state transitions, and repeated episodes of play.

That makes this more than a toy navigation demo. It is a compact example of how reinforcement learning turns a sequence of choices into a policy, with the model gradually preferring actions that lead to better returns.

The README says the project was built in Python with