Nintendo Learning Environment http://olab.is.s.u-tokyo.ac.jp/~kamil.rocki/nintendo/

• New environment for accelerating research in AI, targeting transfer and meta learning
• Performance oriented ( high FPS for learning, efficient parallel execution )
• GB emulator (CPU, GPU (coming soon) and FPGA versions), Support for over 1000 games in total
• NES emulator (coming soon)

bibtex:

@misc{kamil_rocki_2019_2536648,
  author       = {Kamil M Rocki},
  title        = {Nintendo Learning Environment},
  month        = jan,
  year         = 2019,
  publisher    = {GitHub},
  journal      = {GitHub repository},
  doi          = {10.5281/zenodo.2536648},
  howpublished = {\url{https://github.com/krocki/gb}},
  url          = {https://github.com/krocki/gb}
}

Quick start

1. Build C lib make gameboy.so
2. Run the front end from python: python gameboy.py --rom {PATH_TO_ROM}

C dependencies

None

Python dependencies

Numpy, CFFI

Usage

Use the provided python wrapper. Example:

• Run the environment for 0.5M steps
• Produce a 50 frame-long gif every 30s

python gameboy.py --rom ./gb_roms/Micro_Machines_\(USA\,_Europe\).gb --framelimit=500000 --write_gif_every 30 --write_gif_duration 50

Output:

time: 00h 00m 30s, frames 0.06M
time: 00h 01m 00s, frames 0.12M
time: 00h 01m 30s, frames 0.18M
time: 00h 02m 00s, frames 0.24M
time: 00h 02m 30s, frames 0.30M
time: 00h 03m 00s, frames 0.36M
time: 00h 03m 30s, frames 0.42M
time: 00h 04m 00s, frames 0.48M

GIFS generated every 30s:

Generate lots of frames and save them to gif and npy files:

python make_gifs.py --rom {PATH_TO_ROM}

For example, running the command python make_gifs.py --rom ./wario_walking.gb will result in a file like this:

Alternatively, build a standalone gameboy with GLFW support and play games manually

To build:

make

To play:

./gameboy {PATH_TO_ROM}

enter - START
space - SELECT
Z     - A
X     - B
+ arrows (left, right, down, up)
SHIFT - turbo mode