Skip to main content
gym-ple
******

PyGame Learning Environment (PLE) is a learning environment, mimicking the 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.
This package allows to use PLE as a gym environment.

Installing everything
---------------------
gym_ple requires PLE, to install PLE clone the repo and install with pip.

.. code:: shell

git clone https://github.com/ntasfi/PyGame-Learning-Environment.git
cd PyGame-Learning-Environment/
pip install -e .


PLE requires PyGame installed:

On OSX:

.. code:: shell

brew install sdl sdl_ttf sdl_image sdl_mixer portmidi # brew or use equivalent means
conda install -c tlatorre pygame=1.9.2 # using Anaconda

On Ubuntu 14.04:

.. code:: shell

apt-get install -y python-pygame

More configurations and installation details on: http://www.pygame.org/wiki/GettingStarted#Pygame%20Installation

And finally clone and install this package

.. code:: shell

git clone https://github.com/lusob/gym-ple.git
cd gym-ple/
pip install -e .

You also can install it from PyPi:

.. code:: shell

pip install gym_ple


Example
=======

Run ``python example.py`` file to play a PLE game (flappybird) with a random_agent (you need to have installed openai gym).

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gym_ple-0.3.tar.gz (4.0 kB view details)

Uploaded Source

File details

Details for the file gym_ple-0.3.tar.gz.

File metadata

  • Download URL: gym_ple-0.3.tar.gz
  • Upload date:
  • Size: 4.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for gym_ple-0.3.tar.gz
Algorithm Hash digest
SHA256 4a17fdb9d83b481b7d2859c62836783914ad2f51d439b06270b18ac528efa290
MD5 d932b766cd4e72353b228f0fa1fb087d
BLAKE2b-256 0dd641e3510bf943a6383b89d1b3db785efbd2c63c55ac2947a4ba31bf9c9009

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

0.3 This release

1 file

0.2

1 file

0.1

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page