The goal of this project is to develop an AI Bot able to learn how to play the popular game Snake from scratch. In order to do it, I implemented a Deep Reinforcement Learning algorithm. This approach consists in giving the system parameters related to its state, and a positive or negative reward based on its actions. No rules about the game are given, and initially the Bot has no information on what it needs to do. The goal for the system is to figure it out and elaborate a strategy to maximize the score - or the reward. We are going to see how a Deep Q-Learning algorithm learns how to play snake, scoring up to 50 points and showing a solid strategy after only 5 minutes of training.
This project requires Python 3.6 with the pygame library installed, as well as Keras with Tensorflow backend.
To run the game, executes in the snake-ga folder:
python snakeClass.pyThis will run the agent. The Deep neural network can be customized in the file DQN.py. To run the pre-trained agent, uncomment line 21 in DQN.py, so that the network loads the weights for the neural network. In case you want to train your own network, leave it as it is.
In snakeClass.py you can set display_option = True and speed = 50, if you want to see the game running.