Machine & deep learning in mobile video game AI development

Somebody once thought about what if machines could learn independently and improve from experience using data without any human programming or assistance. This notion later came to be known as Machine Learning and that somebody was Arthur Samuel. In the past five years, Machine Learning for Game development has come a long way due to the substantial amount of data accessible for machines to memorize, deep learning algorithms to learn to produce unique content, overcome intriguing challenges, and build realistic worlds.

Game development involves designing, development, and release of a game for entertaining the user – the world. It is wholly an art of creating enticing games. The intricate creation is a process that requires experts in their field like a programmer, sound designers, artists, and graphic designers, along with laborious work, oodles of money, and befitting execution.

Machine Learning in video games has a significant impact on how a video game could turn out. Over the last years, technology has swayed gaming needs, and people’s diverse preferences have led to innovation and evolution in the video game sector.

An individual plays games to have fun, but there’s a lot more than just the fun part. Video games help step up a human’s brain functions, involve continuous engagement of cognitive skills, and release a chemical called serotonin in the brain, also called the happy compound. Innovative technologies like ML and more make games more creative, immersive, and satisfactory, setting a path to revolutionize game development.


It starts by creating a learning agent with the necessary knowledge that learns from experiences, and it comprises certain elements.

  • A learning element that alters the agent’s behavior to make improvements in its performance.
  • Critic, just as the word itself, provides feedback to the agent on how well it performs as regards a fixed standard.
  • A performance element is responsible for choosing the action based on suggestions from an external factor for improvements.
  • A performance analyzer examines the performance of the agent. Accordingly, it provides feedback for improvement to the learning element and whether or not there is scope for enhanced performance by modifying the performance element.

The strategies and techniques that are developed by the critic’s observation and the performance analyzer’s suggestion are executed by the learning agent determining the performance of the cognitive machine learning in the gaming industry and others. The enhanced usability of AI and its subset, ML, makes AI the most profitable sector. Thus, Machine Learning adds logic and experience to the games.

Ray Kurzweil, an American inventor and futurist quoted “Artificial Intelligence will reach human levels by around 2029. Follow that out further to, sat, 2045, and we will have multiplied the intelligence – the human biological machine intelligence of our civilisation – A billion-fold.”

Source: SpringBoard


The system is fed relevant information based on which decisive future predictions can be made using Reinforcement Learning, Deep Learning, or any other ML technique. Games like Atari, Doom, Minecraft showcase the most notable application of machine learning techniques in game playing.

When machines learn from the behavior of others by subjects to large sets of data, it is considered as Deep Learning in games. This technique focuses majorly on the Artificial Neural Network (ANN) and uses multiple layers to extract information from an input to learn and solve complex tasks.

Reinforcement Learning uses a reinforcement agent that is trained depending upon the problem, using rewards or punishments. This reinforcement agent provides suggestions or decides what to do to perform the given task. It lets machines understand the difference between right and wrong and collect the right information to maximize the reward. This technique is used in methods like Q-learning, Deep Q-networks, policy search, etc. It works great in the field of game development.

Convolutional neural networks (CNN) involve specialized ANNs used to analyze data by learning translation-invariant patterns (not dependent on location). It can learn visual data, making it an extensively used tool for deep learning in the gaming industry.

Long short-term memory (LSTM) is a sort of recurrent neural network (RNN) that is used in deep learning. Its applications lie in functions like connected handwriting recognition, speech recognition, and anomaly detection in network traffic or IDSs (intrusion detection system).


From developing complex systems to AI & ML algorithm playing as NPCs (Non-player characters), from video games becoming more exquisite to NLP (Natural Language processing) creating more realistic conversational video games, advancements in Machine Learning have enhanced the algorithms capable of supporting creativity – the creation of not just games but music, art, and more. Let’s crawl into a few use cases of ML but concerning video gaming only.


Machine learning is enhancing at a promising rate. It becomes challenging when it comes to supporting game design and development and personalization of the gaming experience based on the data collected related to the player’s behavior. Whereas, AI developers are trying to use AI to make the game look and feel more realistic where players can interact naturally with other players and the environment. While on the other hand, the developer can achieve the intended player experience. The motive is to enhance an individual player’s experience during the game, and even after. Some tools are used to evaluate a player’s experience. Minor details and lower-level game design choices like the choice of GUI elements, game structure, sound, mechanics, story, visual embellishments, etc. contribute immensely to a player’s highly immersive experience.


Game developers have been leveraging machine learning and data analytics to build the best gaming experiences, which will attract more players to the game. With video game development on the rise, there has been a generation of massive amounts of data that is used to yield insights used for improvements and developments.

The crux behind data collection for game development is capturing the graphical display and recording the user’s data so that those inputs can be studied by learning algorithms to generate optimized results.

It enables data-driven gaming design concepts to make it easier to generate excellent experiences to make video gaming popular across the globe. One a game design is developed, the testers gather people’s response towards the game which is used further to improve game design.

As per the reports, game designing is one of the most profitable professions, a very competitive sector. By learning the ways, your game design can be improved, and you can always ensure to generate beneficial models.


Earlier, the opponents that a player used to fight against were pre-scripted NPCs. Still, with Machine learning-based NPCs, the game has become more uncertain and unpredictable for that gamer. And the unpredictability increases as the learning agent studies your behavior making the game all the more interesting as the opponents become smarter by observing and learning the player’s actions.

Major game development companies are working on machine learning-based NPCs applications where algorithms learn four times faster than reinforcement training.


Complex systems are developed with codes and specialized tools to build a gaming world that is more real and practical. Game app developers pay close attention to detail and work on presenting minute information so that images stand out dynamically. The player is able to interact with its environment and the opponents. NLP also achieves this objective differently.


The traditional game developers can skill up their ML techniques with the growing demand in the industry. The technologies and innovations take the scope of game development a notch up with the potential and possibilities machine learning brings into its arena. Nevertheless, Developers will make smarter and realistic games with their technical skills and creativity and will bring a change in a way the games are created in the coming times.

Logic Simplified, a company based out of Dehradun, has ML game developers researching, refining, and applying AI into their game development. They take it as an exciting opportunity to extend video games into new horizons by giving gamers even more immersive experiences and more playable and unexpected content with intelligent gaming. For more information get in touch with us or email at

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2 comentários em “Machine & deep learning in mobile video game AI development

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