Deciding on which game updates take priority should be a data-driven decision. The impact of any change on the user activity and game revenue are often measured through A/B testing.
A large gaming company requested a self-learning platform which automatically creates and launches hundreds of A/B testing campaigns, learns and iterates over the resulting statistics to reach an arbitrary set of campaign goals.
Data Labelling & Processing & Integration into RL Environment
Copy of Training & Tuning Cycle pt.2
Deep Learning Researcher
Integration & A/B Testing & Deployment
When tested product goes through various stages of its life-cycle, goals might
Most of the tests are created manually and managed by humans, with only few parameters changed at once not to introduce over-complexity into results’ interpretation.
Different parameters, competing goals, lots of statistical events are the factors that introduce additional complexity and need automatic insights extraction.
Finding complex interaction between the entire list of A/B parameters and real-world state is extremely technically challenging.
Our team used recently introduced techniques from reinforcement and deep learning to model complex parameters interaction, time dependent external state and A/B campaign events stream.
As a result, we have developed a self-learning solution which, firstly, pre-trains itself using historical data and then takes control over the campaigns. Product owner remains in control of goals and parameters.
System creates hundreds of micro-campaigns with different goals and fast-checks hypotheses on a user base sample. Then, it explores the resulting space with deeper tests to maximize the outcomes in each cycle.
Campaign results are integrated back via learning process and checked against updated goals; the cycle repeats.
AI explores sophisticated dependencies in parameters/state spaces and their relations with the list of goals, provides opportunities beyond conventional approach to A/B testing, while the rest is controlled by product owner in an intuitive way.
First, we tested, validated and then applied the approach to the mobile game application.
Then we have compared our solution to Bayesian optimization reference and found that our reinforcement learning solution shows superior results to Bayesian baseline.
Furthermore, the self-learning solution is adaptable to completely different cases with relative ease.
Full results presentation is available upon request.
Yuliya is a co-founder and COO of DataRoot Labs, where she oversees operations, sales, communication, and Startup Venture Services. She brings onboard business and venture capital experience that she gained at a leading tech investment company in CEE, where she oversaw numerous deals and managed a portfolio across various tech niches including AI and IT service companies.