Chess Agent Using Reinforcement Learning and Game-Tree Search

A chess agent that combines reinforcement learning with game-tree search.

A chess agent that combines reinforcement learning with game-tree search. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are cooperating agents with defined roles and tools, data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses LLM APIs, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.