- The paper presents AG-PPO, an algorithm that integrates cognitive appraisal dimensions into policy optimization to emulate emotional disorders in RL agents.
- It demonstrates that reward-shaping configurations can significantly improve generalization and agent behavior in dynamic grid world scenarios.
- The results reveal that specific setups induce traits akin to OCD and Anxiety, providing insights into simulating human-like psychological responses.
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World
This research paper presents a method for modeling psychological disorders in reinforcement learning (RL) agents through an innovative Appraisal-Guided Proximal Policy Optimization (AG-PPO) algorithm. The methodology delineated leverages cognitive appraisal theory to provide a more nuanced approach to training RL agents in simulated environments. The investigation primarily focuses on dynamic grid world scenarios where RL agents exhibit emotional traits parallel to those observed in psychological conditions such as Anxiety Disorder and Obsessive-Compulsive Disorder (OCD).
Methodology
The core contribution of this research lies in the design and implementation of the AG-PPO algorithm, which incorporates multiple cognitive appraisal dimensions—Motivational Relevance, Certainty, Novelty, Goal Congruence, Coping Potential, and Anticipation—into the policy optimization process. This approach enables the simulation of emotional stability by aligning quantitative reinforcement learning methods with qualitative cognitive evaluations.
The paper explores several reward-shaping configurations within the AG-PPO framework to regulate agent behavior and simulate specific disorder-like characteristics. Configurations such as RSv1 through RSv7 integrate varying levels of motivational relevance, coping potential, and goal congruence into the reward optimization strategy, aiming to maximize or minimize different appraisal factors. This adaptability allows the agents to respond in ways that mimic psychological conditions under investigation.
Results and Analysis
Significant numerical results were achieved, demonstrating the efficacy of AG-PPO over standard PPO algorithms. Notably, the RSv1 configuration displayed superior generalization abilities, surpassing the baseline performance in terms of average return and success rate across varied test environments. Specific configurations also induced agents to exhibit traits indicative of OCD and Anxiety, evidenced by increased distractions, stress levels, and aversive actions.
Further analyses of agent behavior patterns confirmed that emotional traits reminiscent of human-like psychological disorders could be effectively emulated. For instance, agents trained under the RSv7 configurations exhibited repetitive exploration patterns and anxiety avoidance strategies analogous to OCD tendencies, thereby supporting the role of reward shaping guided by cognitive appraisals.
Implications and Future Directions
Given the analysis, the implications of the proposed appraisal-guided reinforcement learning approach are twofold. Practically, it offers a robust mechanism for developing emotionally cognizant AI systems capable of better mirroring human psychological states, thereby holding the potential for improving human-computer interaction in complex, decision-critical domains. Theoretically, it propels research into dissecting the cognitive-emotional interplay within AI, offering a simulated platform for psychologists and AI researchers to examine the nexus between emotional intelligence and behavioral output.
Future developments could explore extending this integration to other neuropsychiatric conditions, potentially creating a computational understanding of more complex human emotions within AI agents. There is also scope for enhancing the scalability and efficiency of these algorithms in real-world AI applications through further optimization and scenario diversity.
In summary, the research contributes a significant stride towards understanding and simulating emotional intelligence in reinforcement learning, expanding the horizons of both artificial intelligence and psychological explorations in this dynamically evolving field.