Dots-Boxes-Reinforcement
Developing an intelligent agent to play a game of dots and boxes optimally using Q-learning.
Additionally making use of PyTorch to write the neural network code. Training for various network structures, learning rates activation functions, etc. and then exploring each model's predictions.
The entire project is documented as a Jupyter notebook.