This project replicates DeepMind’s AlphaGo Zero and AlphaZero approaches to train a chess-playing agent using deep reinforcement learning. It uses a neural network trained via self-play combined with Monte Carlo Tree Search for move selection. The implementation uses Keras and TensorFlow as the underlying deep learning frameworks.