An OpenAI Gym environment for Chrome Dino / T-Rex Runner Game

This environment utilizes a forked version of Chrome Dino, also called T-Rex Runner, extracted from chromium offline error page. See here.

You can install gym-chrome-dino from PyPI by either

pip install gym-chrome-dino

or

git clone https://github.com/elvisyjlin/gym-chrome-dino.git
cd gym-chrome-dino
pip install -e .

You can get started as follows:

import gym
import gym_chrome_dino
env = gym.make('ChromeDino-v0')

To create a headless (without opening browser) environment

env = gym.make('ChromeDinoNoBrowser-v0')

• The observation is a RGB numpy array with shape of (150, 600, 3).
• The available actions are 0: do nothing, 1: jump, and 2: duck.
• A positive reward 0.01 is given when the dinosaur is alive; a negative penalty -1.0 is given when the dinosaur hits an obstable, which might be a cactus or a bird.

For the DeepMind DQN recipe, where we give 4-stacked resized grayscaled frames (80, 160, 4) to the agent, we provide a wrapping method make_dino(). It also comes with a timer wrapper, which reports the interval between env.step().

from gym_chrome_dino.wrappers import make_dino
env = make_dino(env, timer=True, frame_stack=True)

An instance of DinoGame is created when the environment is made. There are some useful methods for fine control of the training environment. The DineGame can be accessed as follows:

env.unwrapped.game

DinoGame provides a get_score() method to get the score of current game.

score = env.unwrapped.game.get_score()

By default, the acceleration of the game is set to zero. If you want to restore the original acceleration value, please do set_acceleration(True). On the other hand, set_acceleration(False) sets the value to zero.

env.unwrapped.game.set_acceleration(True)

Here is a simple example to use gym-chrome-dino.

import gym
import gym_chrome_dino
from gym_chrome_dino.utils.wrappers import make_dino
env = gym.make('ChromeDino-v0')
env = make_dino(env, timer=True, frame_stack=True)
done = True
while True:
    if done:
        env.reset()
    action = env.action_space.sample()
    observation, reward, done, info = env.step(action)

gym-chrome-dino runs the game on chromedriver via selenium because it is a proper way to monitor and to play Chrome Dino. As a result, the latest chromedriver executable file will be downloaded to the current working directory where your program is.