Decoding Gamer Behavior: Leveraging Inverse Reinforcement Learning to Unveil Personalized Reward Structures in Complex Gaming Environments

This research proposes a novel approach to understanding gamer behavior in complex gaming environments by utilizing Inverse Reinforcement Learning (IRL) to decode the underlying reward structures that motivate player actions. Unlike traditional methods that often rely on predefined reward functions, IRL allows us to infer these reward structures directly from observed behavior, providing a deeper and more nuanced understanding of why players make certain decisions, such as continuing to play, purchasing ability-enhancing items, or quitting the game.

IRL models share a conceptual relationship with structural models, which are commonly used to model decision-making processes by explicitly defining the structure of the decision problem, including the reward functions, state transitions, and choice probabilities. However, structural models often require assumptions about the form of the reward functions and other elements of the model, which may not accurately reflect the true motivations behind player behavior. In contrast, IRL offers a significant advantage by inferring the reward structures directly from observed behavior without relying on these strong assumptions. This allows for the extraction of reward functions that are more closely aligned with actual player motivations, leading to a more accurate representation of the decision-making process.

Understanding these diverse reward structures enables the design of more personalized gaming experiences that align with each player's intrinsic motivations. For instance, games could dynamically adjust challenges, rewards, or recommendations based on the inferred reward structure of the player type, thereby enhancing player engagement, retention, and satisfaction. Additionally, this approach facilitates the study of how these reward structures evolve over time as players' preferences and behaviors change, offering valuable insights into long-term player engagement strategies. By leveraging the strengths of IRL, this research aims to advance our understanding of gamer behavior in ways that traditional structural models may not fully capture.