Machine Learning for Video Games: Present and Potential Applications

Key takeaways

• Machine learning (ML) helps video game developers create dynamic experiences and higher-quality games more efficiently.

• Games with machine learning can help developers better understand user behavior and create more personalized experiences.

• ML for games assists with content moderation and with generating new content to develop larger games that users will play for longer.

Machine learning methods include reinforcement learning, supervised and unsupervised learning, and semi-supervised learning. Discover more about machine learning for video games and the benefits this technology presents for developers. If you’re ready to get your start, the Machine Learning Specialization from Stanford and DeepLearning.AI will give you the opportunity to develop your skills in model training, applied machine learning, reinforcement learning, model evaluation, and more.

Why use machine learning for video games?

Implementing machine learning in video games can help developers create higher-quality, more entertaining, and dynamic game experiences that adapt to the player’s in-game choices and actions. By using machine learning both in the game design itself and in the game development lifecycle, game developers can bring their products to market faster and for a lower cost.

Machine learning in video games is not a new idea, but technological advancements in both industries have increased the options for developers to implement AI and machine learning in their game development process. For example, cloud gaming and virtual and mixed reality games are two of the trends driving AI in video games.

How is machine learning used in video games?

Video game developers can use machine learning in both gameplay and the game development process. According to the 2025 Unity Gaming Report, 96 percent of game development studios are integrating AI tools into their workflow [1]. What’s more, developers appear to feel good about AI, with 79 percent reporting positive feelings about the technology [1].

What are the 4 types of machine learning?

Machine learning uses various techniques to handle data and create algorithms to automate various functions. The four core types of machine learning include supervised, unsupervised, reinforcement, and semi-supervised learning.

Business intelligence and player engagement

Game designers can use artificial intelligence to better understand player behavior and to create personalized and more engaging experiences. Knowing how players interact with a game allows the developers to adjust events within it to increase its realism. For example, when playing the FIFA soccer game, the game uses an algorithm to determine the compatibility between the players on the team you assembled and then informs you regarding how well these personalities will work together.

This sense of realism can enhance gameplay. For a gamer who prefers to experience a more realistic soccer match, this feature can provide a more personalized game, and a happier player tends to be a more engaged one.

Purchase recommendations

Machine learning can also give you insight into the best method of optimizing your pricing model for games. Some pricing models for games let you access the game for less (or free) and then provide you with opportunities to make in-game purchases. Machine learning can help you learn your players’ behavior and build recommender systems to predict what purchases players will make and to balance monetization with keeping players engaged.

Game development lifecycle

Developers can use machine learning to make the game development lifecycle as a whole more efficient, especially with the ability to automate testing for bugs. Machine learning can automate tedious tasks like game testing and feedback analysis and allow your team to focus on tasks that add higher value to your software, such as creating a vision for the product, developing concepts, and prioritizing features. This can shorten the time span in getting games to market and provide clients with a higher quality product.

AI NPCs

Machine learning and artificial intelligence can power non-playable characters (NPCs) to adapt and respond to the player’s in-game choices. For example, machine learning allows NPCs to have unique conversations with players based on previous interactions. Or, machine learning can allow AI opponents to learn from the player's strategy and adapt their difficulty level accordingly.

Complex game mechanics

Machine learning can enable developers to create complex game mechanics that consider many different variables to adjust the outcome of the game. For example, characters can use navigation systems to traverse huge and complex maps, and the character animations will vary based on how they move over the different parts of the terrain. During the development process, companies can also utilize machine learning algorithms and data analysis to gain a better understanding of player preferences in order to upgrade game mechanics and adjust difficulty levels.

Or, a game might replicate a complex interaction from real life. For example, if you’ve repeatedly deceived an NPC in the game, that character begins to trust you less. Conversely, if you’ve been kind to the NPC, maybe it will offer you help later in the game.

Enhanced graphics

You can use machine learning and artificial intelligence to not only provide a smoother and more realistic visual experience in a game, but also to enhance the game by allowing the player to affect the physicality of the world. Machine learning can allow the graphics of the game to change based on a player’s actions. For example, when your character steps into a river, the water ripples accordingly. Machine learning programs can also transform low-quality images into ones with incredible, realistic details, such as leaves on trees and lines on buildings, which can save artists and animators significant amounts of time.

Game content generation

Developers can use AI and machine learning for procedural content generation (PCG), which is a technique for creating in-game content like maps, landscapes, levels, quests, or character interactions based on the decisions the players make. PCG algorithms permit the creation of almost infinite versions of the game, allowing every player to experience the game slightly differently based on their choices and actions. For example, in the game No Man’s Sky created by Hello Games, the developers used PCG to build 18 quintillion unique planets, and each one contains different landscapes, weather, animals, and plants.

Benefits of ML for games

Machine learning also offers benefits for game developers and players that are difficult to achieve in other ways, such as moderating content in real time and keeping up with the size and complexity of modern games.

Moderate content

Multiplayer games like Roblox and Call of Duty allow players to communicate in-game through voice and text chat options. Developers can use machine learning algorithms to detect hateful, violent, bullying, or other types of language that go against community standards. The game can automatically deliver warnings, temporary bans, or other consequences for players who use this type of language.

Build bigger games

The competitive nature of the gaming industry means that game makers have to go further to stand out. One trend in gaming is to make increasingly larger and larger games, offering players more content, like additional storylines or characters. You can see evidence of this in the median size of files built using Unity, a popular game engine. Since 2022, the average build size of Unity projects has grown by 67 percent [1].

Despite using many different methods to keep players engaged, developers find that in-game content, such as daily rewards and missions, is still the most effective way to keep players playing longer, and the results seem to be working. Eighty-eight percent of developers reported that their average playtimes are rising, meaning that players are spending more time playing video games [1].

Machine learning and AI could be the key for video game developers to generate the level of content needed to remain competitive and keep players engaged. They could also decrease the time required to develop a game and get new titles in players’ hands faster.

How to use machine learning and AI in game development

Implementing machine learning and artificial intelligence into the game development lifecycle can help you deliver higher-quality products to your clients or customers with less development time and generate more great ideas for your team to implement.

AI can touch every part of the software development lifecycle, from automating tedious or cyclical tasks to generating code and content, generally minimizing human error, and allowing engineers and developers to spend more time working on satisfying and complex challenges. A few examples of how you might integrate machine learning into game development include:

• Generating code: You can use machine learning and AI to generate parts of your code or to autocomplete code as you type it, making the process of writing code faster and less prone to error.

• Quality assurance and testing: You can use generative AI to automate parts of the game testing process to identify bugs and other issues faster. Machine learning algorithms can also help you create more enjoyable games by identifying areas where players get frustrated and providing you with insight into how to build a more balanced experience.

• Project management: Machine learning tools can help you create a more efficient project management process by automating scheduling tasks and estimating how long tasks will take.

• Security: You can use software development security tools to detect and mitigate security risks, such as vulnerabilities in your code. Machine learning algorithms can also detect suspicious user behavior patterns to identify cheating players.

Read more: AI for Game Development: What Is It and How to Use It?

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