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Super Hexagon Gym Environment

An OpenAI Gym environment for the classic (imho) Terry Cavanagh game SuperHexagon

Usage

Note, that I have only tested on linux and for a variety of reasons, this probably won't work on other platforms.

The interface is the OpenAI gym environment interface. The rgb_array rendering method is the only one supported right now. The action space is Dicrete(3) and the observation space is Box(low=0, high=255, shape=(768*scale_factor, 480*scale_factor, 3), dtype=np.uint8) (scale_factor described later). You can look in the tests directory for some example code. The following are the environment keyword arguments accepted:

argument description
superhex_dir the path to the root of your installation of Super Hexagon. I'm pretty sure it's not legal to distribute the game, so you have to provide it yourself. Can also be set through the SUPERHEXAGON_DIR environment variable.
videodriver determines the SDL_VIDEODRIVER environment variable passed to the Super Hexagon process. Defaults to offscreen. Set to the empty string to make the game visible, assuming your SDL build supports that.
start_process Defaults to True. Whether to actually start a new Super Hexagon game process or try to connect to an existing shimmed process. Useful for debugging the shim.
superhex_log When false pipe the Super Hexagon subprocess output to /dev/null. Defaults to False
scale_factor The Super Hexagon game will render at 768 by 480 by default. This determines the size of the observation made by the environment (useful to scale down when used as input to neural networks). Defaults to .16666 making the observed resolution 128 by 80

Installation

System Dependencies

You need to have SDL (and development headers) and EGL libraries (see) installed in order to build the native code. Additionally, using the gym requires bspatch to be installed.

Python Dependencies

Run pip install -r requirements_test.txt

Building

Running make install should attempt to build the native shim as well as the python library. The python library is installed by running python setup.py install

Test that everything worked by running pytest tests/. This will run a few pretty basic tests on the environment.

How

TODO

  • Document other environment keyword args
  • Document installation (don't forget to mention native dependencies like sdl and bspatch and ffmpeg for training)
  • Document training with baselines zoo and tensorboard
  • Document changes made in zoo fork
  • Document how it works

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OpenAI Gym for the game Super Hexagon

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