Maze AI
CNNs, autoencoders, and RL for maze solving. Best AI course project.
Built convolutional, autoencoder, and reinforcement-learning models for maze solving, trained on a dataset of 120k mazes. The project compared how different model families learn spatial structure and planning.
Awarded best project in SUTD’s Artificial Intelligence course.