Game overview
This game is developed by Team Shadow Agent, a multidisciplinary team of 5 second-year graduate students from Entertainment Technology Center, Carnegie Mellon University, working with Google Stadia.
It is an experimental project exploring new gameplay interactions using reinforcement learning. By working with Google Stadia, the team seeks new game genres and different potential applications.
Reinforcement learning (RL) is a relatively new machine learning method which learns the best actions based on reward or punishment in an environment. The goal of the game is to present a delightful user experience by creating believable characters in a game using existing machine learning tools to train different behaviors of Non-Player Characters (NPCs).
Utilized the 3 different kinds of characteristics of the trained agents, Baby Hippo Spring is a sandbox game of an eco-system simulation where players drag and drop animals and rock into environment to observe unexpected and natural results.
Reinforcement learning (RL) is a relatively new machine learning method which learns the best actions based on reward or punishment in an environment. The goal of the game is to present a delightful user experience by creating believable characters in a game using existing machine learning tools to train different behaviors of Non-Player Characters (NPCs).
Utilized the 3 different kinds of characteristics of the trained agents, Baby Hippo Spring is a sandbox game of an eco-system simulation where players drag and drop animals and rock into environment to observe unexpected and natural results.
Developer
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