Game developers who are looking to search for ways of improving user experience, securing high engagement rates, and maximizing retention to meet financial goals and accelerate revenue.

We apply Reinforcement Learning, as well as other Machine Learning algorithms like generative adversarial networks and clustering algorithms to model multiple diverse scenarios for balancing the game and content generation, user behaviour, retention and revenue prediction. Such a cocktail of RL and ML is selected to achieve the best possible results as it provides us with the necessary artificial intelligence to be able to mimic real users behaviour and decision making and by aligning with the major monetization trends provide personalized well balanced game content in real-time which leads to critical game metrics increase. Predicting in-game and ad revenue is performed by another set of ML algorithms.

AI Agents are virtual agents taught and trained with the help of Reinforcement Learning to achieve different goals, from beating the game, performing a unique set of tasks, enhancing gameplay immersion, to helping improve the gaming development experience and balance, including retention and monetization. In short, AI Agents can be used for:

• Automated Content Generation
• Game Testing & Balancing
• In-Game Player Avatars (Mimicking real users up to 99% of accuracy)
• QA automation
• And much more.

To train AI agents efficiently, we developed a unified training pipeline. Here’s the 3-step pipeline:

Determine the action space. These actions can be discrete, like pressing a button or continuous as in the value within an interval. Actions can be as many as you’d like to have performed simultaneously, and keep in mind that some actions can be invalid under specific conditions.

Determine the reward. The reward must correspond to the goal we want to accomplish—for example, a positive reward when winning and a negative reward when losing. You can also give a positive reward for each action that leads to a target or a negative reward when moving far away from the target.

Define the Deep Reinforcement Learning algorithm. The speed of the agent’s learning capabilities depends on the algorithm as well as whether the agent can learn at all. For each task, it’s necessary to study the features and how applicable they are in unique contexts.