Video Game AI Competition Team

TNT members involved in this project:
Florian Kluger, M.Sc.
Christoph Reinders, M.Sc.
Prof. Dr.-Ing. Bodo Rosenhahn
Frederik Schubert, M. Sc.
Show all

You like video games and are fascinated by artificial intelligence? Then this is the right place for you, because the Institut für Informationsverarbeitung (TNT) started an AI Competition Team and is looking for motivated students. Here you have the chance to apply the knowledge you have learned at university. You can also earn credit points in the form of a student research project (Studienarbeit).

The aim is to develop AIs for selected games and participate in competitions with it.

Interested or still have questions? No problem, just contact us under: ai-battle@tnt.uni-hannover

Overview of Awards

Battlesnake 2021 2nd place Winter Classic Invitational 2021 (Elite)
Battlecode 2020 7th place from over 600 teams
Battlesnake 2019 Winter Classic Champion
Battlecode 2019 9th place from over 600 teams
Battlesnake 2019 2nd place (intermediate category)

Further information and video streams


BattleSnake is based on the classic video game Snake. Each player controls a snake on a 2D grid and has to ensure that it does not collide with itself, another snake, or the borders of the game. Additionally, the snake has a finite number of health points which decay over time but can be topped up by eating food which randomly pops up. Several snakes compete against each other at once, and the last snake to survive wins.

The annual BattleSnake competition in Victoria, BC, Canada regularly welcomes hundreds of contenders. Those interested can either compete individually or in teams of up to five persons and win cash prizes up to $1000. 
In addition, it is now possible to challenge other snakes from all over the globe on




Battlecode is a real-time strategy game, in which two teams, each consisting of up to four members, compete against each other to defeat the AI of the competitor. The competition is organized in five sub-tournaments (Sprint Tournament, Seeding Tournament, Qualifying Tournament, Newbie und Final Tournament). In the Final Tournament, cash prizes totaling $50,000 can be won.

Battlecode can be programm in JavaScript / TypeScript, Python, and Java. JavaScript and Python are used the most. In January, the software and specification of the game rules are released. Each year, the game differs slightly to allow a fair start for each team.

On the Battlecode website online lectures as well as the source codes of the winners of the past years can be found. This allows a relatively easy entry.

Since fall 2018, we additionally organize a Machine Learning for Game AIs lab at TNT. In the first part the fundamental knowledge is learned - especially in the area of path planning, machine learning and reinforcement learning. In the second part, each team develops an AI for Battlecode.


General Video Game AI


The General Video Game AI Competition (GVG-AI) is about developing an agent who can not only play one particular game well, but all of them.  The agent should be able to see for himself what game he is playing and how he has to play it.

Besides the "Single Player Learning Track", where an agent should learn to play different games like in the other competitions, there are other tracks, which cover different aspects of learning. The full list of tracks is:

  • Single Player Learning Track

  • Single Player Planning Track

  • 2-Player Planning Track

  • Level Generation Track

  • REule Generation Track

The organizers of the Competition offer a large framework with various games for training and testing. The framework is written in Java using the Video Game Definition Language (VGDL), but there is also a Python environment for the learning track. Several tracks are usually combined into one competition at conferences dealing with video games. All you have to do is submit your own agent in time via the official website.


  • One agent for many games

  • Different "Tracks"

  • Programming languages: Java or Python

  • Competitions take place at conferences around the world



Battlecode 2020 (7th place from over 600 teams)

Our team name: Bagger288

Battlecode Final 2020 on YouTube


Battlesnake Winter Classic Champion

Our team name: Niedersächsische Kreuzotter

Watch Battlesnake Tournament Day 1 on YouTube

Watch Battlesnake Tournament Day 2 on YouTube

Watch Battlesnake Tournament Day 3 on YouTube


Battlecode 2019 (9th place from over 600 teams)

Our team name: Wololo

Battlecode Final 2019 on YouTube


2nd Place at Battlesnake 2019

Our team name: Niedersächsische Kreuzotter

Watch Battlesnake Final on Twitch

  • Conference Contributions
    • Maximilian Benedikt Schier, Niclas Wüstenbecker
      Adversarial N-player Search using Locality for the Game of Battlesnake
      INFORMATIK 2019, September 2019