Kiiiiii123/SnakeGameAI

Using Deep Q Learning to play Snake game.

Introduction

---

In this tiny project, we will first re-engrave the game Snake with pygame, then turn the game into a reinforcement learning environment with a few minor code changes. In the core section, we will train an RL agent with Pytorch to play the game well — the snake can eat quite a bit of food while the game speed is very fast 😉.

SnakeGameAI
├─ README.md
├─ agent.py
├─ arial.ttf
├─ game_env.py
├─ helper.py
├─ model
│    └─ model.pth
├─ model.py
├─ requirements.txt
└─ snake_game.py

Environment

---

• Observation Space

[danger straight, danger right, danger left,

 direction left, direction right,
 direction up, direction down,

 food left, food right,
 food up, food down
 ]

Take the following figure for example:

• Action Space

[1, 0, 0] -> go straight
[0, 1, 0] -> turn right
[0, 0, 1] -> turn left

• Reward Function

eat food:  +10
game over: -10
else:        0

Agent

---

• Deep Q Learning

• Model

• Bellman Equation

• Q Update Rule Simplified

• Loss Function

Experiments

---

After training for about 100 games, we can get a pretty good agent：