AI TOURNAMENT
Our very first AI Tournament just ended, and it was amazing!
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!
Leaderboard
Updated every Tuesday
Watch the live event on Twitch
Final Standings
| Rank | Name | Score |
| _ | ||
| 1 | Kenshiro | 12.73 |
| 2 | rssalessio | 7.70 |
| 3 | Trrrrr | 4.43 |
Tournament Standings
| Rank | Name | Score |
| _ | ||
| 1 | Kenshiro | 12.65 |
| 2 | Trrrrr | 4.23 |
| 3 | rssalessio | 2.37 |
| TO THE FINAL EVENT | ||
| 4 | helga | |
| 5 | Pierre | |
| 6 | Anu | |
| 7 | CharyMachine | |
| 8 | Chicago Junkman | |
| 9 | drlux | |
| 10 | Ema42 | |
| 11 | Furio19 | |
| 12 | galacticor | |
| 13 | HashArt | |
| 14 | JeyDi92 | |
| 15 | King Arthur | |
| 16 | muraatozbek | |
| 17 | Noob | |
| 18 | ralami | |
| 19 | Riju06 | |
| 20 | robocop | |
| 41 | wizardOfRobots | |
| 22 | sash | |
| 23 | Shivz | |
| 24 | Sumitnc12594 | |
| 25 | TeamWindNet | |
| 26 | The Trainer | |
| 27 | Valetudo | |
| 28 | VlaDiPooH | |
| 29 | Water007 | |
| 30 | wegfawefgawefg | |
| 31 | STARKBOT | |
| 32 | Maestrodigatto | |
| 33 | mohithtaker | |
| 34 | cblee | |
| 35 | nviada | |
| 36 | Djordje | |
| 37 | My Joy | |
| 38 | Kitadake | |
| 39 | Rao | |
| 40 | Amathlog | |
| 41 | Sakthi | |
| 42 | KingCob | |
| 43 | geronimo | |
| 44 | ankit7921 | |
| 45 | Quinton Starck | |
| 46 | Alwahsh | |
| 47 | Derezzed | |
| 48 | Adxenix | |
| 49 | syveqc | |
| 50 | pastellic | |
| 51 | ganman | |
| 52 | junhill | |
| ... |
Episodes
A collection of the best episodes in which we evaluated AI algorithms submitted by participants.
Description
Train an AI agent to effectively play Dead Or Alive ++.
It will face the standard COM player, in the run for completing
the game: seven stages plus the final boss.
The environment, with Tournament-specific settings, can be found
in the dedicated section of DIAMBRA repository
here.
Inside it, a complete submission example is also provided, featuring
a baseline random agent.
The repository also contains python scripts and notebooks showing how to
easily interact with it, together with a working implementation
of a Reinforcement Learning agent as a starting baseline for reference.
Public leaderboard will be updated once a week, with submissions
evaluation performed live and streamed on our
Twitch channel
every Tuesday at 10 PM CET
After competition end, the best agents will take part in
the Final Event and evaluated live for the final standing definition. It will be broadcasted on
Reinforcement Learning Zurich meetup
Getting started
Three easy steps:
1) Get the environment from GitHub
Inside you find python notebooks with
everything you need: from environment usage up to a working baseline PPO RL agent
2) Join the
discord server
You'll meet other participants and the
whole community, with a dedicated channel for support
3) Refer to "Getting started" video tutorials here
They provide additional
info on coding aspects and implementation details
and you are ready to fight.
Timeline
| Start Date | April 20th, 2021 |
| Competition Presentation Event | April 27th, 2021 - RLZ Meetup |
| Weekly Submission Deadline | Every Sunday 11:59 PM UTC |
| Weekly Submissions Evaluation | Twitch Live every Tuesday 10 PM CET |
| Final Submission Deadline and End Date | July 5th, 2021 11:59 PM UTC |
| Final Live Event | July 7th, 2021 - RLZ Meetup |
Prizes
Kindly offered by Reinforcement Learning Zurich!
| 1st Place | 1000 CHF |
| 2nd Place | 300 CHF |
| 3rd Place | 100 CHF |
Submission
An example of a valid submission can be found in DIAMBRA Environment repository
here. It is composed by:
1) An
agent.py
python file containing a python class with the proper
constructor, a reset(self)
method called when env.reset() is called, and a
act(self, observation, info)
method returning actions to be executed
2) A
model.xxx
file containing trained agent model parameters (e.g. policy network weights, if any)
3) A
requirements.txt
file containing python prerequisites to create
the virtual environment (either virtualenv or conda) containing all
modules to run the agent (if any)
4) A
packages.txt
file containing OS packages dependencies required to run the agent (if any)
Evaluation
The evaluation metric is the total cumulative
reward collected by the agent in an episode. Thus,
every contender will be able to estimate its own performance
simply running the agent in local.
Environment default reward function will be used to evaluate
submissions: it is directly proportional to characters
health variation (positive when the agent hits the opponent and
negative otherwise).
Agent performances, averaged on five runs,
will be used to define leaderboard standings.
Technical Details
Observation space
A stack of the last four game frames (pixels) plus a fifth additional channel
containing the following complementary info (numerical data):
last 12 actions (one-hot encoding), own and opponent health,
own and opponent side (Left/Right), stage number and selected character
(one-hot encoding).
Action spaces
Four possible action spaces, depending on the choice of Discrete
VS Multi Discrete and With VS Without Attack Buttons combinations,
resulting in 12, 16, 36, 72 different possible actions.
Rules
Competition related
- Only one account per participant allowed
- Participants are not allowed to share code
- If participants want to team up, they are allowed to submit their agent
from a single user profile belonging to one member of the team
- Maximum two members per team
- Only one submission per week allowed (the latest one will be considered)
- Only one final submission allowed (the latest one will be considered)
Game related
- Every available character can be used
- The agent can be character-specific
Software related
- Action space can be chosen by participants as they prefer,
selecting it among previously described ones
- Observations can be modified by participants inside the
agent
act(self, observation) method, before
feeding it to the agent policy, but it has to mantain an
interface with the observation format described above
- The submitted agent must be compatible with Linux Ubuntu 18.04 OS
- Maximum GPU RAM usage for inference: 1.25 GB
- Maximum inference time (considering also observations modifications,
if any): 50 ms on Nvidia 1050
Failing to comply with these rules will result in agent performance discarding
(if violating RAM usage for example) and/or participant disqualification
(if violating competition rules).
Competition-Specific Terms
Competition Name (the "Competition"): AI Tournament
Competition Organizer: DIAMBRA | Dueling AI Arena
Competition Sponsor: Reinforcement Learning Zurich (Nonprofit)
Competition Website:
https://diambra.artificialtwin.com/aitournament/
Prizes: 1400 CHF
- First Prize: 1000 CHF
- Second Prize: 300 CHF
- Third Prize: 100 CHF
WINNER LICENSE TYPE: Non-Exclusive License
Competitions are open worldwide, except that if
you are a resident of Crimea, Cuba, Iran, Syria, North Korea,
Sudan, or are subject to Italian/EU export controls or sanctions,
you may not enter the Competition. Other local rules and regulations
may apply to you, so please check your local laws to ensure that
you are eligible to participate in skills-based competitions.
The Competition Sponsor reserves the right to award alternative
prizes where needed to comply with local laws.
ENVIRONMENT DISCLAIMER
As specified on Terms of Use (Section 8)
and General Competition Rules (Section 7D),
Environments provided by Competition Organizer and/or Competition Sponsor,
are a mere software interface to existing videogames, and they
cannot work as a standalone application. As such, they require
the User, or the Competition Participant, to own software elements
protected by copyright, and to interface them with the Environment
itself. It is the case, for example, of Game ROMS required to
execute the correspondent Game-related Environment. In such
cases, it is sole an only responsibility of the User, or Competition
Participant, to comply with all the laws and regulations, and to
make sure he has the right to use such copyright-protected material.
Competition Organizer and Competition Sponsor will spend their
maximum effort in avoiding illegal distribution of such material,
and are by no mean responsible for copyright infringement.
Entry in this competition constitutes your acceptance
of these official competition rules.
The Competition named above is a skills-based competition to
promote and further the field of machine learning. You must
register via the Competition Website to enter. Your competition
submissions ("Submissions") must conform to the requirements
stated on the Competition Website. Your Submissions will be
scored based on the evaluation metric described on the Competition
Website. Subject to compliance with the Competition Rules,
Prizes described on the Competition Website, if any, will be
awarded to participants with the best scores, based on the merits
of the machine learning models submitted. See also
General Competition Rules