Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning

Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning

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Conventional personality assessment methods depend on subjective input, while game-based AI predictive methods offer a dynamic and objective framework.However, training these models requires large and labeled datasets, which are challenging to obtain from real players with diverse personality traits.In this paper, we propose a multi-agent system using Deep Reinforcement Learning in a game environment to generate the necessary labeled data.Each agent is trained with custom reward functions based remedy essentials zinc paste on the HiDAC system that encourages trait-aligned behaviors to emulate specific personality traits based on the OCEAN personality trait model.The Multi-Agent Posthumous Credit Assignment (MA-POCA) algorithm facilitates continuous learning, allowing agents to emulate behaviors through self-play.

The resulting gameplay data provide diverse, high-quality samples.This approach allows for robust individual and team assessments, as agent interactions reveal the dale duby obsidian knives impact of personality traits on team dynamics and performance.Ultimately, this methodology provides a scalable, unbiased methodology for human personality evaluation in various settings, establishing new standards for data-driven assessment methods.