gym-simpletetris

Simple Tetris is a simple Tetris environment built entirely in Python. Several options are provided for modifying the environment and its reward system.

Installation

pip

The preferred installation of gym-simpletetris is via pip:

pip install gym-simpletetris

Clone the project

The code is easy to read and modify. If you wish to modify the package to your needs, then follow these instructions:

1. Fork the project
2. Download it using:

git clone https://github.com/<YOUR-USERNAME>/gym-simpletetris

3. Make your changes
4. Install it using pip:

cd gym-simpletetris
pip install -e ./

Usage

You can create an environment using gym.make and supplying the environment id.

import gym
import gym_simpletetris

env = gym.make('SimpleTetris-v0')
obs = env.reset()

episode = 0
while episode < 10:
    action = env.action_space.sample()
    obs, reward, done, info = env.step(action)
    
    if done:
        print(f"Episode {episode + 1} has finished.")
        episode += 1
        obs = env.reset()

env.close()

Environment

Several options are provided to modify the environment. Arguments can be used at the creation of the environment:

import gym
import gym_simpletetris

env = gym.make('SimpleTetris-v0',
               height=20,                       # Height of Tetris grid
               width=10,                        # Width of Tetris grid
               obs_type='ram',                  # ram | grayscale | rgb
               extend_dims=False,               # Extend ram or grayscale dimensions
               render_mode='rgb_array',         # Unused parameter
               reward_step=False,               # See reward table
               penalise_height=False,           # See reward table
               penalise_height_increase=False,  # See reward table
               advanced_clears=False,           # See reward table
               high_scoring=False,              # See reward table
               penalise_holes=False,            # See reward table
               penalise_holes_increase=False,   # See reward table
               lock_delay=0,                    # Lock delay as number of steps
               step_reset=False                 # Reset lock delay on step downwards
               )

If using an observation type of grayscale or rgb then the environment will be as an array of size 84 x 84. If using grayscale, then the grid can be returned as 84 x 84 or extended to 84 x 84 x 1 if entend_dims is set to True. The rgb array will always be returned as 84 x 84 x 3. If using an observation type of ram and extend_dims is set then an array of size width x height x 1 is returned.

Note: The default render mode is human. The render_mode parameter does not alter the default render mode. To render using rgb_array then please use the mode option when calling env.render(mode='rgb_array').

Reward Table

Note: penalise_height and penalise_height_increase cannot be used together. If both are enabled then penalise_height will be used.

Note: penalise_holes and penalise_holes_increase cannot be used together. If both are enabled then penalise_holes will be used.

info dictionary

The info dictionary returned by each step includes the following keys: