Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy DynamicsWe introduce a Reinforcement Learning Psychotherapy AI Companion that
generates topic recommendations for therapists based on patient responses. The
system uses Deep Reinforcement Learning (DRL) to generate multi-objective
policies for four different psychiatric conditions: anxiety, depression,
schizophrenia, and suicidal cases. We present our experimental results on the
accuracy of recommended topics using three different scales of working alliance
ratings: task, bond, and goal. We show that the system is able to capture the
real data (historical topics discussed by the therapists) relatively well, and
that the best performing models vary by disorder and rating scale. To gain
interpretable insights into the learned policies, we visualize policy
trajectories in a 2D principal component analysis space and transition
matrices. These visualizations reveal distinct patterns in the policies trained
with different reward signals and trained on different clinical diagnoses. Our
system's success in generating DIsorder-Specific Multi-Objective Policies
(DISMOP) and interpretable policy dynamics demonstrates the potential of DRL in
providing personalized and efficient therapeutic recommendations.
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