Machine Learning (ML) is a technology that has immense potential. It is when AI (Artificial Intelligence) learns from data patterns to predict specific outcomes. The AI looks at vast amounts of data obtained from experimentation and other sources and earns from it to improve its behavior.
The technology has recently experienced widespread usage, particularly in different industries where AI can do what humans cannot do. It is even there in applications and tech solutions.
Another industry that has the same potential in ML is gaming. Gaming has already demonstrated its possibilities in recent years, from beating even expert human players in gameplay to taking over certain processes within game development, such as NPC behavior modeling and procedural generation.
In terms of game development, the latest solution is Unity ML agents.
Unity ML-Agent is an add-on that lets unity3d game development company to create environments to train ML agents in Unity. Developers can choose from existing environments or create new environments from scratch to train their agents.
In addition, Unity ML agents is an open-source project. It has a community of developers working on it, releasing new features and testing them for better usage of machine learning in games created with Unity.
There are several use cases for games with ML agents in Unity:
Game balancing is balancing the different gameplay aspects so that no dominant strategy arises. For example, the designers should ensure that no weapon in the game has an unfair advantage over other weapons so that, eventually, most players gravitate toward one over all the others.
Developers can make Unity ML agents try out different instances to see if any dominant strategy repeats itself. If, for example, the developers run multiple simulations of the same gameplay, and the agents repeatedly go for the same weapon, then there is a chance that that particular weapon is overpowered. The game designers can then balance it out.
The ML agents in Unity can also be used for testing along with manual testing. For example, the agents can play the game several times and discover game-blocking bugs and errors that pop up repeatedly. The testers can then validate these bugs through manual testing, report the bugs to the developers, and get the bugs fixed.
Dynamic gameplay balancing is the method of using AI to balance the gameplay for a more competitive experience. In this method, the NPC characters change their behavior based on the player’s gameplay strategies.
For example, imagine a shooter game where a headshot is a direct kill. If the player repeatedly can get headshots and easily circumvent the opponents, then after a while, the NPCs would change their behavior to reduce the headshots. They would switch to strategies such as wearing protective headgear to make them more competitive, and the player has to explore other strategies.
Unity ML agents primarily use two different types of learning to train their machine learning agents. They are:
Reinforcement learning is a model of learning where the agent explores different options to find the most rewarding among them. Under this method, the agents perform actions at random. If the efforts result in a reward, the agent considers that a positive learning experience.
As more time goes on, the agent collects more data and learns more about the environment through trial and error. Because the more data, the better the agent becomes at getting the expected reward. Eventually, the agent can become an expert at getting rewards by performing the right actions.
The Unity ML tool also lets users run numerous training instances simultaneously. Rather than running a single model and learning from it slowly, the developers can run multiple concurrent training sessions. Unity will take data from all of them simultaneously and make the AI behavior more accurate.
Reinforcement learning is impossible for all instances. In addition, multiple variables sometimes involved can complicate things. For example, if the gameplay instance has very few rewards, teaching the AI through reinforcement learning becomes a hassle.
Instead, imitation learning is a second Unity ML-agent method where the AI learns how to play the game through human demonstrations. Under this model, a human player plays the game, and the AI tries to imitate the behavior at the same time. When the developers repeat the same multiple times, the AI becomes more competent at learning how to play the game.
Machine learning is fast becoming a staple in game development. Many companies have started to use it to improve their processes and, in turn, provide their players with a better gameplay experience. Even more, are adopting it as time goes on.
Unity ML tool will democratize it even further. I will help small-time game developers and companies take advantage of the advances in machine learning without the requirement of huge investments.
Juego Studios is an top game development company. We have a dedicated team of game developers who have in-depth knowledge of different features of the game engine and the C# programming language. Our team has worked on numerous platforms and created games of various genres.