Tillämpning av maskininlärning för skapande av avancerade NPC-beteenden i Unity 3D.

Machine learning has been widely used in computer games for a long time. This is something that has been proven to create a better experience and well-balanced challenges for players. In 2017, the game engine Unity released the ML-agents toolkit that provides several machine learning algorithms together with examples and a user-friendly development environment for free to the public. This has made it simpler for developers to explore what is possible in the world of machine learning in games. In many cases, a developer has spent a lot of time on a specific place in a game and would like a player to visit that area. The location can also be important for the gameplay, but the developer wants to steer the player there without the player feeling forced. This thesis investigates if it is possible to create a smart agent in a modern game engine like Unity that can affect the route taken by a player through a level. The results show that this is fully possible with a high success rate for a simple environment, but that it requires much time and effort to make it work on an advanced environment with several agents. Experiments with a randomized environment to create an agent that is general and can be used in many situations were also done, but a successful agent could not be produced in this way within the timeframe of the work