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ToriLLE

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Toribash Learning Environment

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# ToriLLE
Toribash Learning Environment. Extra "L" to make words more memorable for Finns ("Torille" = "To the marketplace").

ToriLLE provides learning agents an interface to video-game [Toribash](http://www.toribash.com/), a humanoid fighting game.
Toribash provides environment for MuJoCo-like humanoid control, specifically aimed for competitive gameplay. This makes
ToriLLE suitable for e.g. self-play experiments.

ToriLLE comes with a Python interface and pre-made OpenAI Gym environment with various tasks. Following white-paper includes baseline experiments and benchmarks conducted using ToriLLE: [https://arxiv.org/abs/1807.10110](https://arxiv.org/abs/1807.10110)

## Requirements
Tested to work on Windows 10, Ubuntu 16.04 and MacOS 10.13. Tested on Python versions 3.5 and 3.6, and will likely not work on 2.7.

* Numpy (Python)
* FileLock (Python)
* [Wine](https://wiki.winehq.org/Download) (For Linux/MacOS. **Requires modern version. Tested on 3.0.3**)

## Quickstart
Remember to install [Wine](https://wiki.winehq.org/Download) if you are on Linux or MacOS. Make sure `wine` command is defined.

Following will download ToriLLE with stripped down version of Toribash:
```
pip install git+https://github.com/miffyli/torille
```

Random agent:
```python
from torille import ToribashControl
from torille.utils import create_random_actions

# Show gameplay
toribash = ToribashControl(draw_game=True)
toribash.init()

# Random agent
while 1:
    state, t = toribash.get_state()
    if t: break
    toribash.make_actions(create_random_actions())
toribash.close()
```

OpenAI Gym environment:
```python
import gym
import torille.envs

env = gym.make("Toribash-DestroyUke-v0")
# Show gameplay
env.set_draw_game(True)

initial_state = env.reset()
t = False
# Random agent
while not t:
    s, r, t, _ = env.step(env.action_space.sample())
env.close()
```

## Manual installation

:warning: Following instructions are old and may not work with current versions of Toribash. :warning:

You can install ToriLLE without PyPI/pip with the following:

* Install [Toribash](http://toribash.com/) (note: Only Steam version may be up to date)
* Copy contents of `toribash-codes` to Toribash installation directory. Overwrite files
  * **Note:** This will prevent using that specific installation as a regular game. Remove/rename `profile.tbs` file 
               to revert most of the changes and use game normally again.
  * **Note2:** Starting with Toribash 5.4 / Steam version of Toribash, settings file is stored in under user's directory at
               `Saved Games/Toribash/custom.cfg`. Toribash loads this file by default if it finds it, which may cause 
               ToriLLE to run in wrong settings.
* Provide path the installed `toribash.exe` when creating `ToribashControl` objects (if you use provided Python library)

## Playing in multiplayer

:warning: Multiplayer does not work on Linux Wine, and may not work with the new version :warning:

Want to try your agents against human players in multiplayer? Check how [manual remote control](docs/manual_torille.md) works.

## Documentation

Examples in `examples` provide quickstart to how to use ToriLLE, and also show how to apply settings or 
record replays.

For references see:

* [Python library](docs/torille.md)
* [Gym environment](docs/envs.md)

For troubleshooting, see the [FAQ](docs/faq.md).

If you wish to modify ToriLLE or use other language to control Toribash instance, see [hacking](docs/hacking.md) and [documentation on protocol](docs/protocol).

## Repository structure
- `./torille/`: Python codes for the learning environment (inc. Gym environment)
  - `./torille/toribash`: This includes stripped version of the Toribash game when installed from PyPi
- `./toribash-codes/`: Files required for Toribash to make this learning environment work 
- `./examples/`: Python examples on how to use this library
- `./docs/`: Detailed documentation of the inner workings
- `./experiments/`: Codes used to run experiments in the white paper
- `./images/`: Contains GIFs used here

## Related work and useful links

* Options/chat rules: http://forum.toribash.com/showthread.php?t=317900
* Lua functions: https://github.com/trittimo/ToriScriptAPI/blob/master/docs/toribash_docs.txt
* Bodypart list: http://forum.toribash.com/showthread.php?t=9391
* Previous experiments at machine learning with Toribash: 
  * http://forum.toribash.com/showthread.php?t=170100
  * http://forum.toribash.com/showthread.php?t=167355
  * http://forum.toribash.com/showthread.php?t=25263
  * https://www.researchgate.net/profile/Jonathan_Byrne/publication/228848637_Optimising_offensive_moves_in_toribash_using_a_genetic_algorithm/links/0046351420d5001396000000.pdf

## Citing

We wouldn't mind a citation if you find ToriLLE useful in your work. It also helps us to see what people have been up to!

```
@inproceedings{kanervisto2019torille,
  title={ToriLLE: Learning Environment for Hand-to-Hand Combat},
  author={Kanervisto, Anssi and Hautam{\"a}ki, Ville},
  booktitle={2019 IEEE Conference on Games (CoG)},
  year={2019},
}
```

## Special thanks & Acknowledgements
- hampa and Dranix for invaluable help with configuring Toribash and lua scripts (also for developing the game in the first place!)
- Siim Põder (user "windo" on GitHub) for original [toribash-evolver](https://github.com/windo/toribash-evolver) code
- box (Toribash user) for comments during inception of this project

## License 
Code original to ToriLLE is licensed under GNU GPL 3.0. Toribash is property of Nabistudios. Toribash binary in this package is shared with the permission of main developer "hampa".

ToriLLE

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