This is a project I completed as part of the "Practical AI with Python and Reinforcement Learning" Udemy course. The goal of the project was to create a Snake game environment using the OpenAI Gym library, which allows developers to create custom environments for training and testing reinforcement learning algorithms.
To use this Snake game environment, follow these steps:
- Clone the repository:
git clone https://github.com/username/snake-game-environment.git
- Install the necessary dependencies:
pip install gym numpy pygame
To use the Snake game environment, simply import the SnakeEnv
class from the snake_env.py
file:
from snake_env import SnakeEnv
env = SnakeEnv()
The SnakeEnv
class inherits from the OpenAI gym.Env
class, so you can use it in the same way as any other OpenAI environment. For example, to reset the environment and get the initial state:
obs = env.reset()
And to take an action in the environment:
action = 0 # replace with your chosen action
obs, reward, done, info = env.step(action)
The step
method returns four values:
obs
: the new observation (state) after taking the actionreward
: the reward for taking the actiondone
: a boolean indicating whether the episode is over (i.e., the Snake has died or won)info
: any additional information about the environment
If you want to customize the Snake game environment, you can modify the snake_env.py
file. Some possible modifications include:
- Changing the size of the game window and the Snake
- Modifying the reward function
- Adding additional actions or observations
- Changing the game rules (e.g., the Snake's movement speed)
This project was completed as part of the "Practical AI with Python and Reinforcement Learning" Udemy course, which provided guidance on how to create the Snake game environment using OpenAI Gym. The snake_env.py
file is based on the code provided in the course.