In Progress · 2024 – Present
Competitive AI chess with a hybrid classical + neural architecture
A competitive chess engine achieving 2000+ rated play by integrating classical search algorithms with neural network-based evaluation. This hybrid system combines the strategic depth of traditional minimax algorithms with the pattern recognition capabilities of deep learning. Built a hybrid system interfacing optimized C++ move generation and alpha-beta pruning search with a PyTorch neural network for position evaluation, trained on thousands of expert and self-play games, with board representation optimized using bitboards and Zobrist hashing.