Unity Blog

Puppo, The Corgi: Cuteness overload with the Unity ML-Agents Toolkit

Building a game is a creative process that involves many challenging steps including defining the game concept and logic, building assets and animations, specifying NPC behaviors, tuning difficulty and balance and, finally, testing the game with real players before launch. We believe machine learning can be used across the entire creative process and in today’s blog post we will focus on one of these challenges: specifying the behavior of an NPC. Traditionally, the behavior of an NPC is hard-coded using scripting and behavior trees. These (typically long) lists of rules process information about the surroundings of the NPC (called observations) to dictate its next action. These rules can be time-consuming to write and maintain as the game evolves. Reinforcement learning provides a promising, alternative framework for defining the behavior of an NPC. More specifically, instead of defining the observation to action mapping by hand, you can simply train your NPC by providing it with rewards when it achieves the desired goal.