How Automated Game Balancing can Boost your Financial Performance

Why should you balance your game?

As a game developer, you’ve probably had one of the busiest years of your life thanks to the increasing demand for new content, especially with the COVID-19 pandemic that pushed nearly everyone to be at home more often than ever before. To meet these extraordinary demands, one must take extraordinary measures and actions to get stuff done, right?

To top it off, the increase in demand is also coupled with the strain of delivering financial goals and accelerating revenue for your games because no game runs entirely for free.

Both the increase in demand and the strain to accelerate revenue has led us to see a dire need in the industry to deliver games at break-neck speed with outstanding quality, which is easier said than done.

We’ve seen firsthand the core need for games to reach bigger audiences, a lot faster than, let’s say, 5 years ago. So, what can you do, or what can others do for you, to help you speed up your game development process?

Keeping up with the latest trends in game development can be challenging, but there’s no denying that there’s true value in embracing new methods to achieve new, better results. Such is the case of Deep Reinforcement Learning which can automate a number of routine tasks of your game development, freeing up your time to focus on strategy-critical, and creative tasks.

Some of the tasks that Deep Reinforcement Learning can automate include game balancing, which is crucial in game development as it helps offset the strongest traits of a character or strategy with an equally strong drawback in a specific area so as to avoid character or game approach dominance.

Most consider game balance as a mathematical-algorithmic three-pronged model of game numbers, mechanics, and the flow between those two, which is accurate, but down to the nuts and bolts, it’s about how the player experiences the game, that either drives game monetization or it doesn’t.

For game developers and designers, it’s important to walk in the shoes of a player. Would a player really enjoy a game where there’s no way for skills to outsmart or outplay the strongest character? Or what would the user experience be like?

Take Cyberpunk for example, as it, to their dismay, illustrates how game balancing can make or break a game. The kind of hype around Cyberpunk was unlike anything we’ve seen as of late in the gaming industry so it’s no surprise that everyone’s hopes were sky-high.

Unfortunately, due to a massive list of bugs and a severely unbalanced experience, the game flopped massively. It failed so much that it prompted Playstation to issue refunds, an unprecedented event, while Xbox added a label to the game to warn players that they would experience severe bugs.

To some degree, we can’t help but wonder what would’ve happened if the game development studio had paid a little extra time and attention to game balancing. In stark contrast, Halo 2: Anniversary is dubbed as the most balanced game to date where weapons have nuanced weaknesses, players can win fights with skills when facing stronger opponents with better weapons, yet weapons are still powerful, and so on. Now, that’s what a great gaming experience looks like.

The challenges of game balancing

A balanced game that offers an improved gaming experience is one of the most effective means to achieve high engagement rates and maximize retention, which in turn helps accelerate financial growth and drive revenue.

To get a well-balanced gaming experience, there’s a lot of road to cover and lots of effort to invest. For instance, fight-style games only have a finite number of variables incorporated into the game with a relatively simple formula. In some cases, the more powerful a strike, the slower the character will move which is far from ideal as average players will then have an unfair advantage over characters with stronger characteristics.

Balancing games becomes even more difficult in complex games like role-playing games where characters have unique characteristics that need to increase as they reach more levels, introducing more variables for game designers to consider.

Of course, many game developers are forced to resort to tricks or workarounds to overcome some of these balancing difficulties, like giving weak a particularly strong or special attack.

These methods compensate for some of the problems but can also lead to gameplay deviations that can hinder the success of a game. Of course, games are not static and continue to evolve as new versions or patches are continuously released, growing better at balancing characters or game approaches.

Imbalanced games lead to inferior user experiences as players become dissatisfied by game failures. For example, the popular first-person shooter Counter-Strike game has an AWP weapon that is too powerful for its own good, leading to player servers imposing a “no AWP” rule; it all comes down to how the game was ineffectively balanced.

Additionally, poorly balanced games reduce retention rates and user engagement which translates into missed financial opportunities. But as stated earlier, the complexity of balancing a game originates out of the complexity to perceive what needs to be balanced; many wish it were a clean-cut process but, all in all, it mostly comes down to experience.

True, no game is perfectly balanced, but there are close approximations that can make a world of difference for the gaming experience. Take Mario Kart for example. Each character has its strengths and weaknesses, making it more complex to know what would be considered balanced, so the best bet is to engage in a trial-and-error sort of approach.

Game balancing takes on a higher meaning if you consider that gameplay is all about choices. A poorly balanced game will offer pointless choices, quickly exposing stronger strategies and limiting the use of other approaches. A well-balanced game will render your game elements relevant, making sure every choice counts and reverberates across the game’s flow.

Imbalances or imperfections in balancing can crop up anywhere; for example, powerful units can be thoroughly enjoyed by players but if the costs or resource count is high enough, it’ll become too big of an investment, which is far from the goal. Another element that needs to be balanced, and that game designers have a hard time balancing, is maps. Maps need to be uniquely tailored to each experience to ensure there’s no evident dominant strategy popping up.

Also, many have come to rely on game patches as a means to fix what’s broken or put a bandaid on bigger issues. This is a double-edged sword that sometimes pressures you to let a few evident imbalances slip by in one release as you can fix it in the next one.

Do you remember Flappy Bird? Its difficulty was the stuff of legends, pushing players to try their hardest with no real reward. It was simply too hard and, eventually, many people left the game because there was no balance between difficulty and rewards.

Automating game balancing

Let’s circle back up to Deep Reinforcement Learning. Balancing is hard, routine, labor-intensive work that takes a significant chunk of time out of your schedule. Thanks to Deep Reinforcement Learning, it’s now possible to automate this time-consuming process, freeing up your time to focus on more creative concepts of the game design.

So, what exactly does Deep Reinforcement Learning do for your game balancing? In short, it creates AI Meta-Agents* of any skill level so they can play, simulate, analyze, and discern the impact of specific game actions, and adjust unique attributes of those game actions until the game is balanced probabilistically (or by approximation). Let’s break that down a little bit.

Deep Reinforcement Learning models diverse behaviors to balance technique and skills. Through iterations, AI Agents** can tweak the game to manage this balancing act across relevant aspects of the gameplay.

Thanks to their better-than-a-human speed and accuracy, AI Agents can explore unique scenarios, actions, levels, behaviors, and more, from start to finish, performing a comprehensive exploration of the game to provide answers to your game balancing questions.

GameAI™ trains AI Agents that continuously learn about unique game balancing decisions specific to your game’s needs. It focuses on tracking, as quickly and as accurately as possible, the evolution and regressions in performance. Then, it adapts itself so the game remains credible without the player realizing the game is making some elements easier to achieve.

Through innovative algorithm techniques, GameAI’s Deep Reinforcement Learning helps balance games through AI Agents that choose actions, some optimal, some sub-optimal, some neutral, evaluate map functionality, assess the game state, compare difficulty, validate advantages, and more, with the goal of progressively evaluating the overall performance of the game to balance it effectively in hours instead of days.

Wrapping up

So, to sum up:

• Game balancing is crucial for game development as it helps offset the strongest traits of a character or strategy with an equally strong drawback in a specific area so as to avoid character or game approach dominance;
• Deep Reinforcement Learning can automate game balancing with great success, leaving you to focus on world-building, creative tasks;
• Balancing can grow more complex in role-playing games where characters have unique characteristics that need to increase as they reach more levels;
• Workarounds to game balancing can lead to gameplay deviations;
• Imbalanced games lead to inferior user experiences as players become dissatisfied by game failures;
• The complexity of balancing a game originates out of the complexity to perceive what needs to be balanced;
• Balancing is hard, routine, labor-intensive work that takes a significant chunk of time out of your schedule;
• Deep Reinforcement Learning trains AI Agents of any skill level so they can play, simulate, analyze, and discern the impact of specific game actions, and adjust unique attributes of those game actions until the game is balanced probabilistically (or by approximation).

And there you have it! In an environment where it’s becoming more challenging to keep up with the demand and release quality content, we wish you perfectly balanced games to advance your financial performance.

*Notes:

Meta-Agents: A Meta-Agent is a complex set of Neural Networks with a certain level of intelligence that are trained by our GameAI team based on each client’s unique needs and game title. Meta-Agents are constantly learning new mechanics and their intelligence grows simultaneously. Meta-Agents produce Human-like Avatars to play game levels.

Human-like Avatars: Human-like Avatars are a simulation created by the Meta-Agent to play specific game levels.