Chess Engine built with a static evaluation and SVM (default branch is mishbranch) Training is done in train.py, different sessions set different oponents in order to ensure that there is variance in the dataset. Logs for training are found in scr/models/history

also in scr/models/history:

training datasets are: history.csv history2.csv history3.csv

Saved Models: All trained models are saved to .pkl files best performing model is test_model_2.pkl In order to see how to load a .pkl file into a model object see game.py line 252

Testing is done in test.py. Tests performed at depths 1-4 between SVM supported alphabeta agent (called the Markov Agent) and a standard Monte Carlo Search Tree agent. Unfortunately some of the testing logs were lost due to merging errors, but the ones that remain are also found in src/models/history

DEMO:

in order to play against the demo:

python game.py

(User is always the white pieces)

You may first need to set up a conda environment:

conda env create -f env.yml

The stockfish-11-linux folder contains the open source stockfish software, and was used to gather training datasets

utility functions are located in util.py (log function, directory structures etc.)

The following files contain agents: markovsearch.py - contains the SVM driven model based chess agent, this is the main goal of the project alphabeta.py - contains a standard alphabeta search tree based agent using static evaluation function mcts.py - contains a standar monte carlo tree search based agent using static evaluation function

The evaluation function is located in:

env.py

In order to run the code in your own environment:

use spec-file.txt or env.yml to set up an anaconda environment

The test folder contains testing for data preprocessing and different ideas, some of which were not used