Elmar999 / snake-reinforcement-DNN

Snake game with Deep Neural Network

Introduction

AI for snake game trained from the states of environment using Neural Network. Neural Network is implemented using Keras functional API, that makes it easy to experiment with different architectures. The input of Neural Network is the state elements of our environment.
We have 8 states in this game:

• Player neighbour on above (1-yes, 0-no)
• Player neighbour on down (1-yes, 0-no)
• Player neighbour on right (1-yes, 0-no)
• Player neighbour on left (1-yes, 0-no)
• Player position over x-axis
• Player position over y-axis
• Food position over x-axis
• Food position over y-axis

Data

Data has always been one of the most important things in AI related problems. In order to teach the player to make an action in each state, data should be well gathered. In order to have a good data I did not play the game randomly but with user-handling keyboard. So I played the game for about 300 episodes and more data generated later on automatically. Reward was given when player eats food and gathered data.

Model

Neural Network input is consist of 8 neurons, which are showed above. Since we have 4 choices in each state (up, down, left, right), the number of output neurons is 4. The architecture of the neural network is not complex, I tried with two hidden layers but you can experiment with other architectures.

Installation

First of all, you need to check if you have all required packages to launch the program. If not then install requirements.

$ cd ../snake_ai_reinforcement
$ pip3 install -r requirements.txt

Then cd into game directory and run python3 main.py -h to see usage and options.

$ cd game
$ python3 main.py -h

Usage

usage: main.py [-h] [--generate_data] [--episode_number FLAG_NB_EPISODE]
               [--train] [--load_data FLAG_LOAD_DATA]
               [--model_path FLAG_MODEL_PATH] [--run_game]

Options

• --generate_data - user should play the game to generate data
  • --episode_number NB_EPISODE - specify number of episodes that the game will be played.
• --train - train a model with Neural Network
• --lad_data load_data_path - if you have several generated data you can specify the data that you want to train on
• --run_game - run the game
  • --model_path model_path - specify which model (.h5 file) that game will be used during prediction time

Usage example

generating data

$ python3 main.py --generate_data --episode_number 300

train model over generated data

$  python3 main.py --train --load_data ../game/snake_data.npy

run game with trained model

$ python3 main.py --run_game --model_path ../game/snake_model.h5

Game Demo

Bot is playing on his own.