The Phillip AI

An SSBM player based on Deep Reinforcement Learning.

NOTE: This project is no longer active and is subject to bit-rot. There is a successor project based on imitation learning from slippi replays at https://github.com/vladfi1/slippi-ai.

Requirements

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

1. The dolphin emulator. You will 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. You will need NTSC 1.02.
3. Python 3. On Windows, you can use Anaconda which sets up the necessary paths. You can also use the linux subsytem on Windows 10.
4. Install phillip. You can download and extract a zip file or clone this repository. Then, from the phillip root, run pip install -e ..
5. Some trained agents are included in the agents directory. The full set of trained agents is available here.

Play

You will need to know where dolphin is located. On Mac the dolphin path will be /Applications/Dolphin.app/Contents/MacOS/Dolphin. If dolphin-emu is already on your PATH then you can omit this.

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

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. The best human-like agent is delay18/FalcoBF, available in the Google Drive zip.

Windows Notes

• The --exe will be the path to the Binary\x64\Dolphin.exe you unzipped. In general, the forward /s should be back \s for all paths, unless you are using MinGW, Cygwin, git bash, or some other unix shell emulator.
• You may need to omit the 3 from commands like python3 and pip3.
• If not using Anaconda, you will likely need to modify your PATH so that python is visible to the command prompt.
• Communication with dolphin is done over the local loopback interface, enabled with the --tcp 1 flag (now implied by --windows). You may also need to open port 5555 in your firewall.
• If on Windows 10 you can do everything in the Linux subsystem and follow the linux instructions, except for obtaining dolphin. You will need to pass in an explicit user directory with --user tmp (the temp directories that python creates start with /tmp/... and aren't valid for windows dolphin).

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 which uses zmq to synchronize with the AI - 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).

Training on Windows is not supported.

Thanks to microsoftv there is now an instructional video as well!

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 altf4 for getting me started, and to spxtr 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).