While many chess computers display impressive superhuman performance, they often require massive computational ressources and/or extensive expert level knowledge and finetuning during their development. In this project, I wanted to challenged this supposition and developed a personal chess AI on my own using reinforcement learning together with a Monte Carlo tree search planning algorithm, similar to AlphaZero [1]. The backend is written in C++, utilizing its strong computing performance, whereas a Python frontend allows human players to compete against the AI interactively.

[1] Silver, David, et al. “Mastering chess and shogi by self-play with a general reinforcement learning algorithm.” arXiv preprint arXiv:1712.01815 (2017).