Steps and deliverables for initial presentation/report

1. Create problem statement

2. Look through python libraries for chess engines/neural networks

3. Find background/literature review

4. Extract dataset, and create dataset description

5. Create project plan

6. Compare/contrast and select on a chess model

7. Program and implement the model

8. Create and format report/presentation

Links to python libraries and other resources

Steps to set up repo

1. Install python

2. Do pip install chess (for windows python -m pip install chess)

3. Download and extract data file from https://database.lichess.org/#standard_games (suggest using older data)

TODOS

1. Write parsing instructions and helper functions for pgn files parsing

2. Add pytorch libraries and functionality

3. Create chess interface

4. Create machine learning model

5. Train the AI against pgn files through lichess dataset. Use reinforcement learning using legal moves against best moves.