  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.
