# Reinforcement Learning Methods for Emulating Personality in a Game Environment +
## 2 Literature Review
### Related Work  
A number of alternative models have been investigated [19], but the OCEAN model is still the most popular personality framework in AI applications because of its simplicity and empirical robustness. Despite doubts about its scientific validity, the MBTI has been used in adaptive interfaces and conversational agents [20]. Early affective computing used [PERSON]'s PEN model [21], which highlights the biological foundations of personality. Although it has garnered attention for modeling ethical behavior, the HEXACO model--an extension of OCEAN with an additional honesty-humility dimension--is still not widely used in AI [22]. Finally, the DISC model has been used on occasion, particularly in business applications, in gamified personality simulations and AI coaching [23]. While none of these models challenge OCEAN's hegemony in empirical AI research, they do demonstrate the variety of methods for incorporating personality into intelligent systems. Previous research has explored the development of adaptive agents in serious game environments. Notably, a multi-agent system was integrated into the SIMFOR project, a serious game designed for crisis management training [24]. The agents in this system are based on the belief-desire-intention (BDI) deliberation model and include configurable parameters that support scenario design and crisis event simulation. This allows designers to define agent behaviors in accordance with specific crisis management scenarios. While this approach demonstrates sufficient flexibility to be adapted to various serious gaming contexts, its primary aim is scenario construction rather than establishing a standardized methodology for non-player character (NPC) development. The development of DreamerV3, a general algorithm that outperforms specialized methods in over 150 diverse tasks with a single configuration, is a recent accomplishment of impressive use of DRL for video game play [25]. Using robustness techniques based on normalization, balancing, and transformations to establish learning in multiple domains, DreamerV3 learns a model of the environment and enhances its behavior by imagining future scenarios. When used directly, DreamerV3 is the first algorithm capable of gathering diamonds in Minecraft without the need for human data or curriculum. This has been presented as a major AI challenge that calls for investigating long-term strategies in an open world using sparse rewards and pixels. Even in intricate 3D maps found in an Ubisoft AAA game created with the Unity engine, a novel method has shown promise [26]. More precisely, a complex architecture was used in conjunction with the SAC algorithm, which minimizes the actor loss function, to train a model-free RL model. A 3D occupancy map, a 2D depth map, absolute goal and agent positions, and additional state variables like relative goal position, speed, acceleration, and prior action are among the various input types that are part of the model's architecture. To produce the final embedding that is shared by both, each of these input layers is passed through separate feature extraction layers, and the combined output is then fed through multiple linear layers and an LSTM. Additionally, all NPCs in the prototype roguelike game DeepCrawl are managed by policy networks that have been trained using DRL algorithms [27]. Each player has a different experience in this game as agents learn to keep the player from winning while remaining realistically competitive and still beatable. In parallel, other works have utilized game environments to simulate and analyze agent behaviors. For instance, [28] examines how a single agent's movement within a simplified virtual room can reflect behavioral tendencies aligned with the Openness personality trait. Similarly, Refs. [29,30] investigates the potential of escape room gameplay as a means of inferring player personality through interactions with puzzles and riddles.