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https://github.com/ntasfi/PyGame-Learning-Environment
PyGame Learning Environment (PLE) -- Reinforcement Learning Environment in Python.
https://github.com/ntasfi/PyGame-Learning-Environment
agent ai artificial-intelligence deep-reinforcement-learning game machine-learning pygame python reinforcement-learning research
Last synced: about 2 months ago
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PyGame Learning Environment (PLE) -- Reinforcement Learning Environment in Python.
- Host: GitHub
- URL: https://github.com/ntasfi/PyGame-Learning-Environment
- Owner: ntasfi
- License: mit
- Created: 2016-02-23T18:13:39.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2022-01-19T22:19:02.000Z (over 2 years ago)
- Last Synced: 2024-05-21T11:50:33.479Z (4 months ago)
- Topics: agent, ai, artificial-intelligence, deep-reinforcement-learning, game, machine-learning, pygame, python, reinforcement-learning, research
- Language: Python
- Homepage:
- Size: 7.87 MB
- Stars: 996
- Watchers: 39
- Forks: 232
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyGame-Learning-Environment
**PyGame Learning Environment (PLE)** is a learning environment, mimicking the [Arcade Learning Environment](https://github.com/mgbellemare/Arcade-Learning-Environment) interface, allowing a quick start to Reinforcement Learning in Python. The goal of PLE is allow practitioners to focus design of models and experiments instead of environment design.
PLE hopes to eventually build an expansive library of games.
**Accepting PRs for games.**
## Documentation
Docs for the project can be [found here](http://pygame-learning-environment.readthedocs.org/). They are currently WIP.
## Games
Available games can be found in the [docs](http://pygame-learning-environment.readthedocs.org/en/latest/user/games.html).
## Getting started
A `PLE` instance requires a game exposing a set of control methods. To see the required methods look at `ple/games/base.py`.
Here's an example of importing Pong from the games library within PLE:
```python
from ple.games.pong import Pong
game = Pong()
```
Next we configure and initialize PLE:
```python
from ple import PLE
p = PLE(game, fps=30, display_screen=True, force_fps=False)
p.init()
```
The options above instruct PLE to display the game screen, with `display_screen`, while allowing PyGame to select the appropriate delay timing between frames to ensure 30fps with `force_fps`.
You are free to use any agent with the PLE. Below we create a fictional agent and grab the valid actions:
```python
myAgent = MyAgent(p.getActionSet())
```
We can now have our agent, with the help of PLE, interact with the game over a certain number of frames:
```python
nb_frames = 1000
reward = 0.0
for f in range(nb_frames):
if p.game_over(): #check if the game is over
p.reset_game()
obs = p.getScreenRGB()
action = myAgent.pickAction(reward, obs)
reward = p.act(action)
```
Just like that we have our agent interacting with our game environment.
## Installation
PLE requires the following dependencies:
* numpy
* pygame
* pillow
Clone the repo and install with pip.
```bash
git clone https://github.com/ntasfi/PyGame-Learning-Environment.git
cd PyGame-Learning-Environment/
pip install -e .
```
## Headless Usage
Set the following in your code before usage:
```python
os.putenv('SDL_VIDEODRIVER', 'fbcon')
os.environ["SDL_VIDEODRIVER"] = "dummy"
```
Thanks to [@wooridle](https://github.com/ntasfi/PyGame-Learning-Environment/issues/26#issuecomment-289517054).
## Updating
`cd` into the `PyGame-Learning-Environment` directory and run the following:
```bash
git pull
```
## Todos
* Documentation is currently in progress.
* Tests
* Parallel Learning (One agent, many game copies)
* Add games
* Generalize the library (eg. add Pyglet support)
## Citing PLE
If PLE has helped your research please cite it in your publications. Example BibTeX entry:
```
@misc{tasfi2016PLE,
author = {Tasfi, Norman},
title = {PyGame Learning Environment},
year = {2016},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ntasfi/PyGame-Learning-Environment}}
}
```