Skip to content

Latest commit

 

History

History
52 lines (29 loc) · 2.47 KB

Readme.md

File metadata and controls

52 lines (29 loc) · 2.47 KB
Raw

The Phillip AI

An SSBM player based on Deep Reinforcement Learning.

Requirements

Tested on: Ubuntu >=14.04, OSX, Windows 7/8/10.

  1. The dolphin emulator. Probably need to compile from source on Linux. On Windows you'll need to install a custom dolphin version - just unpack the zip somewhere.

  2. The SSBM iso image. Must be NTSC 1.02.

  3. Python 3.

  4. Install phillip. This should pull in python dependencies like tensorflow.

    pip3 install -e .

Play

You will need to know where dolphin is located. On Mac the dolphin path will be ~/../../Applications/Dolphin.app/Contents/MacOS/Dolphin. On Windows it will be the path to the .exe you unzipped, and you will need the --tcp 1 option.

python3 phillip/run.py --gui --human --start 0 --load agents/FalconFalconBF --iso path/to/SSBM.iso --exe path/to/dolphin [--tcp 1]

Trained agents are stored in the agents directory. Aside from FalconFalconBF, the agents in agents/delay0/ are also fairly strong. Run with --help to see all options.

Train

Training is controlled by phillip/train.py. See also runner.py and launcher.py for training massively in parallel on slurm clusters. Phillip has been trained at the MGHPCC. It is recommended to train with a custom dolphin from https://github.com/vladfi1/dolphin - the below commands will likely fail otherwise.

Local training is also possible. First, edit runner.py with your desired training params (advanced). Then do:

python3 runner.py # will output a path
python3 launcher.py saves/path/ --init --local [--agents number_of_agents] [--log_agents]

To view stats during training:

tensorboard --logdir logs/

The trainer and (optionally) agents redirect their stdout/err to slurm_logs/. To end training:

kill $(cat saves/path/pids)

To resume training run launcher.py again, but omit the --init (it will overwrite your old network).

Support

Come to the Discord!

Recordings

I've been streaming practice play over at http://twitch.tv/x_pilot. There are also some recordings on my youtube channel.

Credits

Big thanks to https://github.com/altf4/SmashBot for getting me started, and to https://github.com/spxtr/p3 for a python memory watcher. Some code for dolphin interaction has been borrowed from both projects (mostly the latter now that I've switched to pure python).