The Catch the Cheese 2 is a showcase of how AI agent (mouse) itself learns to run away from the cat and collect cheeses in the game. You can play the game at https://bomzj.github.io/catch-cheese-2/dist/.

The mouse uses Q-Learning which is the most popular algorithm in Reinforcement Learning to explore all possible situations on the game field to find an optimal play.

At the beginning, the mouse knows nothing about how to play properly the game and interacts with the environment randomly. The mouse receives rewards or punishments on each its action. This makes the mouse understand which actions were good and which weren't at particular game situations (states). Therefore, the mouse tries to maximize its rewards and avoid punishments during training.

Reward values:

• Idling is -1. We don't want the mouse to wander.
• Collecting a cheese is +100.
• The cat hits the mouse -100.
• The mouse hits the walls -100. We don't want to see the mouse trying to go out of the game field.

I tried different values for rewards and selected ones that work the best.

Input layer contains 16 neurons. At the beginning, I tried lesser number of input features that were continuous variables e.g. coordinates of the cat [3, 8], but were getting Gradient Explosion, so all weights of the network were broken, becoming NaN. I decided to skip stuff like regularization for that and make more features but simple at the same time to boost learning process. So all 16 features are one-hot encoded (0 or 1) and describe position of the cat, cheese and the boundaries of the game field.

I tried different number of hidden layers/neurons, the most optimal was 1 hidden layer with 6 neurons. With that configuration the mouse needs just 2 mins (training automatically stops) to understand the game at decent level to have approximately 98% win rate. Increasing the number of neurons didn't give better results, instead it takes longer to train.

Output layer is vector of Q values for 4 actions, where the mouse can go [left, right, up, down]. An example, [0.9, -1, 0.3, 5], the mouse chooses to go down since Q value for down action is the highest one.

I chose Relu as activation function for hidden layer based on its performance. For output layer I decided not to use activation function since it allows us to have arbitrary values for reward, so it's linear.

I discovered strange situation with long training process of the mouse. Training the model from the scratch for short period of time (2-5 mins) makes it very good at predictions, but if traininig continues right after that, the model becomes absolutely unusable! I'm not sure if it's somehow relates to Overfitting in Machine Learning. So, training it for short period of time makes it pretty good, but continuing training turns it into completely broken stuff. I checked model weights there were no NaNs or 0s. I suppose it's neither Gradient Exlosion nor Gradient Vanishing.

This game uses:

• TensorFlow.js for machine learning in js
• Vue.js as engine for main game loop, game logic, DOM manipulation and rendering
• Tailwind for css styling

The code of this game is well commented and flexible being as Starter Kit or Tutorial. You can implement AI even for cat, add team of cats, bonuses, tweak input features to make mouse move stepwise or adding walls inside the grid to make it as maze.

• NPM
• Node.js

• Chrome Browser
• Visual Studio Code
• Visual Studio Code Extensions:
  • Debugger for Chrome
  • Vetur

npm install

npm run serve

The command starts dev server at http:https://localhost:8080/.

F5 in Visual Studio Code opens up http:https://localhost:8080/ in chrome browser.

npm run build

Compiles project into /dist folder