https://github.com/tristanrussell/gym-simpletetris
A Tetris environment built entirely in Python and compliant with the OpenAI Gym API.
https://github.com/tristanrussell/gym-simpletetris
gym gym-environment python3 reinforcement-learning research tetris
Last synced: about 1 month ago
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A Tetris environment built entirely in Python and compliant with the OpenAI Gym API.
- Host: GitHub
- URL: https://github.com/tristanrussell/gym-simpletetris
- Owner: tristanrussell
- License: mit
- Created: 2022-03-28T09:50:36.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-04-27T14:36:36.000Z (over 3 years ago)
- Last Synced: 2024-10-11T09:11:46.266Z (about 1 year ago)
- Topics: gym, gym-environment, python3, reinforcement-learning, research, tetris
- Language: Python
- Homepage: https://pypi.org/project/gym-simpletetris/
- Size: 2.06 MB
- Stars: 1
- Watchers: 1
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# 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`:
```shell
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:
```shell
git clone https://github.com//gym-simpletetris
```
3. Make your changes
4. Install it using pip:
```shell
cd gym-simpletetris
pip install -e ./
```
## Usage
You can create an environment using `gym.make` and supplying the environment id.
```python
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:
```python
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
| Option | Description |
|:---------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `reward_step` | Adds a reward of +1 for every time step that does not include a line clear or end of game. |
| `penalise_height` | Penalises the height of the current Tetris tower every time a piece is locked into place. A negative reward equal to the height of the current tower is given. |
| `penalise_height_increase` | Penalises an increase in the height of the Tetris tower. Every time a piece is locked into place, if the height increases then a negative reward equal to the increase is given. |
| `advanced_clears` | Changes the rewards for clearing lines to be similar to the modern Tetris system. The new rewards are 100 for a single line clear, 250 for a double line clear, 750 for a triple line clear and 3000 for a Tetris (quadruple line clear). |
| `high_scoring` | Changes the reward given for each line clear to 1000. |
| `penalise_holes` | Penalises the number of holes in the current Tetris tower every time a piece is locked into place. A hole is an empty cell with at least one non-empty cell above it. |
| `penalise_holes_increase` | Penalises an increase in the number of holes in the current Tetris tower every time a piece is locked into place. |
**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:
| Key | Type | Description |
|:----------------|:---------|:-------------------------------------------------------------------|
| `time` | `int` | The time passed (no. of steps) since the start of the current game |
| `current_piece` | `string` | The letter representing the current piece |
| `score` | `int` | The score of the current game |
| `lines_cleared` | `int` | The number of lines cleared in the current game |
| `holes` | `int` | The number of holes in the current Tetris tower |
| `deaths` | `int` | The number of deaths since the environment was created |
| `statistics` | `dict` | The number of Tetris pieces dispatched by type |