DRL is a New AI-disrupt in Gaming Industry


When it comes to mobile app games, and any game for that matter, the golden nuggets of valuable information stem from understanding what the person playing the game is experiencing. Good and bad, it all helps shape a gaming product, helping it be better and more enjoyable. 

Now, with the way the gaming industry behaved last year, it’d be an understatement to say it experienced explosive growth; it was unparalleled and almost a game-like (a post-apocalyptic one) scenario where all players involved were trying to survive in uncharted territory. 

Game developers were doing their best to frantically meet the high demand for content that users were calling for. For context, over 80 billion game downloads took place in 2020, an 18% increase year over year. This translates into users spending  over $100 billion in mobile games in 2020.

The truth of the matter is that players are using mobile devices to play games at an unprecedented rate. With the majority of the world locked up at home thanks to the pandemic, players spent 4.2 hours roughly on a daily basis playing mobile games. This new reality meant that 83% of Android revenue comes from mobile games while the App Store’s revenue share accounted for 66% from mobile games. 

Which types of games take the cake, you might ask? Based on  industry research, the biggest chunk of mobile game downloads goes to casual games with an astounding 78% share. At a more granular level, the top three most profitable games are puzzles, casinos, and RPG. 

What all of this translates into is the fact that mobile game developers are in dire need to reshape and leverage new methods and approaches to generate high-quality content that addresses the needs of gamers. To have a competitive advantage, game developers need to look for the quickest means to release mobile game apps into the wild or continuously update a game with new challenges.

Testing is one of the most critical aspects when it comes to high-quality gaming content.

For years, the traditional way of testing gameplay has been with human playtesters who would report back on any game behavior that would deviate from what it was originally intended to perform like. To this day, playtesters are still widely used, even though it doesn’t come without its trials.

Two of the most common scenarios where game playtesting is used are for difficulty balance and crash testing. For example, adding a new challenge to a game, be it a new level, world, or unlocking a new achievement, to name a few, is typically a threefold endeavor. First, developers design and build the new offering: second, developers reach out to playtesting – a designer, internal testers, then external service providers and make edits as necessary,  third, the new offering is released widespread for end-user consumption. 

Now, the most resource-exhaustive, costly, and time-consuming step is playtesting, by far. On top of being a relatively slow process when compared to the high-pace rhythm of deploying new game offerings, it is also an inaccurate and biased, prone to human error process that can become quite complex.

Enter Artificial Intelligence into the conversation, and voila! What usually takes a week in human playtesting can now take hours or even minutes with automated playtesting. So, what is the name of the actual AI sub-field that is bringing about these outstanding results? 

Deep Reinforcement Learning (DRL), or how we like to call it: the disruptive force of change in the Gaming Industry.

Deep Reinforcement Learning 

With much left to explore, Artificial Intelligence and Machine Learning offer a wealth of opportunities to discover new and more efficient ways for mobile application development. Typically used to streamline processes, AI is a game-changer when it comes to making playtesting faster. From another front, AI-driven fraud detection in game applications is also one of the most fruitful uses of this smart technology to ensure no bad actors bypass security measures placed to protect game developers from massive losses due to fraud.

Game developers live and breathe games. Whether it’s a brand new game or adding improvements to an existing game, testing is front and center in terms of quality assurance. As mentioned earlier, the increasingly demanding need to meet the call for new content is indisputably one of the most trying aspects of playtesting. Playtesting is time consuming and when you’re in a fast-moving industry like mobile gaming, it’s imperative that you use effective, quick methods to ensure no flaws slip through the cracks. Ultimately, exhaustive testing is critical to the success and wellbeing of mobile games.

Fortunately, Deep Reinforcement Learning has come in to act as one of the most effective methods to execute tests at scale with virtual players, generating accuracy beyond what human playtesting can yield. With DRL as part of the playtesting experience, feedback is provided faster, dramatically reducing wait times. All in all, these benefits in joint help improve the game balance, content quality, and the delivery of best-in-class user experience to players.

Another cool aspect of using DRL in playtesting as opposed to human testing is the ability to have more iterations per level, helping game designers fine-tune the game quickly and efficiently.

All in all, DRL helps revenue growth as it focuses on specific game criteria that drives profits. These criteria items include:

Reinforcement learning requires integration components like SDKs and infrastructures, RL agents, and analytics tools. In addition, some of the most prominent aspects of reinforcement learning deal with optimization to make good decisions on its own, the prediction of maximum reward, and learning to behave optimally in unseen situations.

Worth mentioning, reinforcement learning is not applicable in some areas yet. The reasons are: 

Deep Learning Reinforcement helps game developers:

  • Simulate players,
  • Exercise faster tests,
  • Glean more accurate results,
  • Save time and use it to address bugs,
  • Scale gaming tasks,
  • Run as many iterations as needed without the concern of straining resources,
  • Deliver high-quality content quickly.

The impact of Deep Reinforcement Learning is reverberating in an industry that had long needed an efficient way to go about playtesting as it’s typically a costly, slow, and resource-straining process.

Deep Reinforcement Learning is not only bridging the gap between inefficient playtesting, it’s completely reshaping the way game developers approach this value-add process that not only enhances quality, but delivers a superior gaming experience to players.

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