Collection of very simple toy problems in the form of games, solved using traditional AI techniques (A*, backtrack, local search).
All games and algorithms are entirely implemented in Python (with numpy dependency) therefore speed isn't really the focus here as I did this merely for fun (aka educational purposes). Nonetheless, there are a few cool tricks for optimizing stuff (e.g. numpy stride_trick for checking sudoku blocks).
Most if not all games here can be formalized as CSP problems, so most solutions presented here explicitely refer to CSP as presented in the book Artificial Intelligence: A Modern Approach, off of which most implementations are inspired from.
Install with pip install -e . after cloning into repo.
A notable exception is that of Snake, which I (not really) ""solved"" using Deep Reinforcement Learning (simple DQN).
The agent is trained entirely from raw pixels starting from a random policy using a frame-stack of size 4 (as the game graphics is so poor you can't even tell where's the head.. :D ); you can find the hyperparameters and the settings I used for training in the script toy_games/reinforcement_learning/train_rl_baseline.py.
I also provide a Gym Environment implementation for Snake to train your agent on with configurable grid size, rendering options..
Snake pre-trained weights can be loaded using stable baseline 3 utils directly from snake.zip (PyTorch).
Although the agent is far from super-human performance, it still exhibits some cool strategies, maybe one day it will surpass its master..
All games are playable and you can run them by executing the corresponding file/script as in python3 toy_games/games/snake.py for playing snake.
You can then test out how different configuration play out (e.g. size of grid world).
