Abstract
The integration of artificial intelligence across multiple domains has emphasized the importance of replicating human-like cognitive processes in AI. By incorporating emotional intelligence into AI agents, their emotional stability can be evaluated to enhance their resilience and dependability in critical decision-making tasks. In this work, we develop a methodology for modeling psychological disorders using Reinforcement Learning (RL) agents. We utilized Appraisal theory to train RL agents in a dynamic grid world environment with an Appraisal-Guided Proximal Policy Optimization (AG-PPO) algorithm. Additionally, we investigated numerous reward-shaping strategies to simulate psychological disorders and regulate the behavior of the agents. A comparison of various configurations of the modified PPO algorithm identified variants that simulate Anxiety disorder and Obsessive-Compulsive Disorder (OCD)-like behavior in agents. Furthermore, we compared standard PPO with AG-PPO and its configurations, highlighting the performance improvement in terms of generalization capabilities. Finally, we conducted an analysis of the agents' behavioral patterns in complex test environments to evaluate the associated symptoms corresponding to the psychological disorders. Overall, our work showcases the benefits of the appraisal-guided PPO algorithm over the standard PPO algorithm and the potential to simulate psychological disorders in a controlled artificial environment and evaluate them on RL agents.
Abstract (translated)
在多个领域的人工智能整合中，强调人工智能中复制人类似认知过程的重要性。通过将情商引入AI代理中，可以评估其情感稳定性，从而提高其在关键决策任务中的弹性和可靠性。在这项工作中，我们开发了一种使用强化学习（RL）模型建模心理障碍的方法。我们利用评价理论在动态网格世界环境中训练RL代理。此外，我们还研究了各种奖励塑造策略，以模拟心理障碍并调节代理的行为。对各种修改后的PPO算法的比较发现，其 variants能够模拟焦虑障碍和强迫症（OCD）类似行为。进一步，我们比较了标准PPO与AG-PPO及其配置，强调了在泛化能力方面的性能改进。最后，我们对复杂测试环境中的代理的行为模式进行了分析，以评估与心理障碍相关的症状。总的来说，我们的工作展示了评价引导的PPO算法在标准PPO算法之上的优势，以及在一个受控的人工环境中模拟心理障碍并通过RL代理对其进行评估的潜力。
URL
https://arxiv.org/abs/2407.20383
