Data-Driven Optimization of Game Distribution: A Reinforcement Learning Approach

This study investigates the application of Reinforcement Learning models, in particular Deep Q-Networks (DQN), for optimizing distribution strategies in the gaming industry. The motivation for this research stems from the growing need for adaptive and efficient distribution methods in dynamic markets and increasingly complex user expectations. Existing approaches rely predominantly on static models that are often unable to adapt to rapid changes in user behavior and market trends, highlighting a critical gap in the literature for flexible and adaptive methods like Reinforcement Learning.

Methodologically, the study uses a DQN model that utilizes relevant data from platforms such as Steam and Epic Games Store, including variables such as game pricing, number of recommendations and reviews, and playtime metrics. The data was pre-processed and normalized to ensure the stability of the model. The evaluation was based on key performance indicators such as RMSE and MAPE.

The results demonstrate the high predictive accuracy of the model (RMSE: 4.31e-3), despite the challenges associated with data distribution (high MAPE). The model successfully identified optimal distribution strategies while reducing costs and enhancing user engagement. This underlines the potential of Reinforcement Learning in adapting distribution strategies in real time. This study not only confirms the applicability of Reinforcement Learning in optimizing game distribution, but also highlights the need to further enhancements in data quality to increase the performance of the model.