diambra-arena 2.1.0rc4

DIAMBRA™ Arena. Built with OpenAI Gym Python interface, easy to use, transforms popular video games into Reinforcement Learning environments

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DIAMBRA Arena

DIAMBRA Arena is a software package featuring a collection of high-quality environments for Reinforcement Learning research and experimentation. It provides a standard interface to popular arcade emulated video games, offering a Python API fully compliant with OpenAI Gym format, that makes its adoption smooth and straightforward.

It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via Python PIP, as described in the installation section below. It is completely free to use, the user only needs to register on the official website.

In addition, it comes with a comprehensive documentation, and this repository provides a collection of examples covering main use cases of interest that can be run in just a few steps.

Main Features

All environments are episodic Reinforcement Learning tasks, with discrete actions (gamepad buttons) and observations composed by screen pixels plus additional numerical data (RAM values like characters health bars or characters stage side).

They all support both single player (1P) as well as two players (2P) mode, making them the perfect resource to explore all the following Reinforcement Learning subfields:

Standard RL	Competitive Multi-Agent	Competitive Human-Agent	Self-Play	Imitation Learning	Human-in-the-Loop

Available Games

Interfaced games have been selected among the most popular fighting retro-games. While sharing the same fundamental mechanics, they provide slightly different challenges, with specific features such as different type and number of characters, how to perform combos, health bars recharging, etc.

Whenever possible, games are released with all hidden/bonus characters unlocked.

Additional details can be found in the dedicated section of our Documentation.

Dead Or Alive ++	Street Fighter III 3rd Strike	Tekken Tag Tournament	Ultimate Mortal Kombat 3	Samurai Showdown 5 Special	The King of Fighers '98 Ultimate Match Hero

Many more are coming soon...

Index

• Installation
• Quickstart & Examples
• Reinforcement Learning Libs Compatibility
• AI Tournaments
• References
• Support, Feature Requests & Bugs Reports
• Citation
• Terms of Use

Installation

• Create an account on our website, it requires just a few clicks and is 100% free

• Install Docker Desktop: Linux | Windows | MacOS

• Install DIAMBRA Command Line Interface (avoid using a virtual environment*): python3 -m pip install diambra

• Install DIAMBRA Arena (using a virtual environment is strongly suggested): python3 -m pip install diambra-arena

*: If you use [ana]conda and have the base environment active, make sure to deactivate it with conda deactivate

Quickstart & Examples

DIAMBRA Arena usage follows the standard RL interaction framework: the agent sends an action to the environment, which process it and performs a transition accordingly, from the starting state to the new state, returning the observation and the reward to the agent to close the interaction loop. The figure below shows this typical interaction scheme and data flow.

Download Game ROM(s) and Check Validity

Check available games with the following command:

diambra arena list-roms

Output example:

[...]
 Title: Dead Or Alive ++ - GameId: doapp
   Difficulty levels: Min 1 - Max 4
   SHA256 sum: d95855c7d8596a90f0b8ca15725686567d767a9a3f93a8896b489a160e705c4e
   Original ROM name: doapp.zip
   Search keywords: ['DEAD OR ALIVE ++ [JAPAN]', 'dead-or-alive-japan', '80781', 'wowroms']
   Characters list: ['Kasumi', 'Zack', 'Hayabusa', 'Bayman', 'Lei-Fang', 'Raidou', 'Gen-Fu', 'Tina', 'Bass', 'Jann-Lee', 'Ayane']
[...]

Search ROMs on the web using Search Keywords provided by the game list command reported above. Pay attention, follow game-specific notes reported there, and store all ROMs in the same folder, whose absolute path will be referred in the following as your/roms/local/path.

Specific game ROM files are required, check validity of the downloaded ROMs as follows.

Check ROM(s) validity running:

diambra arena check-roms your/roms/local/path/romFileName.zip

The output for a valid ROM file would look like the following:

Correct ROM file for Dead Or Alive ++, sha256 = d95855c7d8596a90f0b8ca15725686567d767a9a3f93a8896b489a160e705c4e

Make sure to check out our Terms of Use, and in particular Section 7. By using the software, you accept the in full.

Base script

Running a complete episode with a random agent requires less than 20 python lines:

 import diambra.arena

 env = diambra.arena.make("doapp")

 observation = env.reset()

 while True:
     env.render()

     actions = env.action_space.sample()

     observation, reward, done, info = env.step(actions)

     if done:
         observation = env.reset()
         break

 env.close()

To execute the script run:

diambra run -r your/roms/local/path python script.py

Additional details and use cases are provided in the Getting Started section of the documentation.

Examples

The examples/ folder contains ready to use scripts representing the most important use-cases, in particular:

• Single Player Environment
• Multi Player Environment
• Wrappers Options
• Human Experience Recorder
• Imitation Learning

These examples show how to leverage both single and two players modes, how to set up environment wrappers specifying all their options, how to record human expert demonstrations and how to load them to apply imitation learning. They can be used as templates and starting points to explore all the features of the software package.

Reinforcement Learning Libs Compatibility

DIAMBRA Arena is built to maximize compatibility will all major Reinforcement Learning libraries. It natively provides interfaces with the two most import packages: Stable Baselines (both version 2 and 3) and Ray RLlib. Their usage is illustrated in detail in the documentation and in the DIAMBRA Agents repository. It can easily be interfaced with any other package in a similar way.

Native interfaces, that can be installed with the dedicated options listed below, have been tested with the following versions:

• Stable Baselines 3 | pip install diambra-arena[stable-baselines3] (Docs - GitHub - Pypi): 1.6.1
• Ray RLlib | pip install diambra-arena[ray-rllib] (Docs - GitHub - Pypi): 2.0.0
• Stable Baselines | pip install diambra-arena[stable-baselines] (Docs - GitHub - Pypi): 2.10.2

AI Tournaments

We are about to launch our AI Tournaments Platform, where every coder will be able to train his agents and compete. There will be one-to-one fights against other agents, challenges to collect accolades & bages, and matches versus human players.

Join us to become an early member!

Our very first AI Tournament has been an amazing experience! Participants trained an AI algorithm to effectively play Dead Or Alive++. The three best algorithms participated in the final event and competed for the 1400 CHF prize.

References

• Documentation: https://docs.diambra.ai
• Paper: https://arxiv.org/abs/2210.10595
• Website: https://diambra.ai
• Discord: https://discord.gg/tFDS2UN5sv
• Linkedin: https://www.linkedin.com/company/diambra
• Twitch: https://www.twitch.tv/diambra_ai
• YouTube: https://www.youtube.com/c/diambra_ai
• Twitter: https://twitter.com/diambra_ai

Support, Feature Requests & Bugs Reports

To receive support, use the dedicated channel in our Discord Server.

To request features or report bugs, use the GitHub Issue Tracker.

Citation

Paper: https://arxiv.org/abs/2210.10595

@article{Palmas22,
    author = {{Palmas}, Alessandro},
    title = "{DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation}",
    journal = {arXiv e-prints},
    keywords = {reinforcement learning, transfer learning, multi-agent, games},
    year = 2022,
    month = oct,
    eid = {arXiv:2210.10595},
    pages = {arXiv:2210.10595},
    archivePrefix = {arXiv},
    eprint = {2210.10595},
    primaryClass = {cs.AI}
 }

Terms of Use

DIAMBRA Arena software package is subject to our Terms of Use. By using it, you accept them in full.

DIAMBRA™ is a Trade Mark, © Copyright 2018-2023. All Right Reserved.