/flappy-bird-gymnasium

An OpenAI Gym environment for the Flappy Bird game

Primary LanguagePythonMIT LicenseMIT

Flappy Bird for Gymnasium

Python versions PyPI License

This repository contains the implementation of two Gymnasium environments for the Flappy Bird game. The implementation of the game's logic and graphics was based on the flappy-bird-gym project, by @Talendar.

State space

The "FlappyBird-rgb-v0" environment, yields RGB-arrays (images) representing the game's screen. The "FlappyBird-v0" environment, on the other hand, yields simple numerical information about the game's state as observations.

FlappyBird-v0

  • the last pipe's horizontal position
  • the last top pipe's vertical position
  • the last bottom pipe's vertical position
  • the next pipe's horizontal position
  • the next top pipe's vertical position
  • the next bottom pipe's vertical position
  • the next next pipe's horizontal position
  • the next next top pipe's vertical position
  • the next next bottom pipe's vertical position
  • player's vertical position
  • player's vertical velocity
  • player's rotation

FlappyBird-rgb-v0

The RGB image of size 288, 512 pixels. The pixel values are from range [0, 255]. The image does not contain score of bird.

Action space

  • 0 - do nothing
  • 1 - flap

Rewards

  • +0.1 - every frame it stays alive
  • +1.0 - successfully passing a pipe
  • -1.0 - dying

         

Installation

To install flappy-bird-gymnasium, simply run the following command:

$ pip install flappy-bird-gymnasium

Usage

Like with other gymnasium environments, it's very easy to use flappy-bird-gymnasium. Simply import the package and create the environment with the make function. Take a look at the sample code below:

import time
import flappy_bird_gymnasium
import gymnasium
env = gymnasium.make("FlappyBird-v0")

obs, _ = env.reset()
while True:
    # Next action:
    # (feed the observation to your agent here)
    action = env.action_space.sample()

    # Processing:
    obs, reward, terminated, _, info = env.step(action)
    
    # Rendering the game:
    # (remove this two lines during training)
    env.render()
    time.sleep(1 / 30)  # FPS
    
    # Checking if the player is still alive
    if terminated:
        break

env.close()

Playing

To play the game (human mode), run the following command:

$ flappy_bird_gymnasium

To see a random agent playing, add an argument to the command:

$ flappy_bird_gymnasium --mode random

To see a Deep Q Network agent playing, add an argument to the command:

$ flappy_bird_gymnasium --mode dqn