In the recent SEA Game Jam 2021, the Singaporean team Cheep Cheep took first place with their game Pawn Shop Detective, which was developed by the team. In this game, you were in charge of running a pawn shop where various kings came in to sell their crowns.
With the theme of “crown” this year, the International Game Developers Association (IGDA) Malaysia wanted to encourage participants to think outside the box. The theme was intentionally vague in order to encourage true creativity from the participants.
Unicorns on Unicycles is a Malaysian-developed game that began life as a game jam experiment and is now scheduled for a full release on PC and mobile devices. Weyrdworks, a Malaysian indie game studio, is responsible for the game’s development.
In the game Unicorns on Unicycles, two players will take on the roles of the titular creatures, dueling against each other with the sharp horns of their respective adversaries. Think of it as a zanier version of the classic arcade game Joust and the indie game Nidhogg combined into one package.
As a result of the additional mechanic of stabilizing the unicorns while they ride their unicycles, this fantastical fighting game takes on the feel of a QWOP-style balancing act. When played in its entirety, the game features a diverse cast of characters, including the festive Pinata and the robotic U9000, among others.
This game is known for its intense one-on-one battles between players, but it also features an extensive single-player campaign that takes players on a journey across the multiverse through 30 levels.
By showing off their work at numerous game jams, Beck and his team discovered that while the core mechanics of Unicorn on Unicycles were in place, there was still more work to be done in order to fully flesh out the game as a full-fledged video game experience.
Machine Learning Agents, for those who are unfamiliar with the term, is an add-on to the Unity game engine that allows developers to imbue their digital avatars with intelligent player behavior through the use of deep reinforcement learning and imitation learning techniques.