We 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.

该研究介绍了一个基于深度强化学习的心理治疗 AI 伴侣，通过患者的反馈为治疗师生成主题推荐，并使用多目标策略生成四种不同精神疾病的策略。实验结果表明，该系统能够捕捉到历史主题的真实数据，并且最佳性能模型因疾病和评分标准而异。通过 2D 主成分分析和迁移矩阵可视化策略轨迹，呈现出具有不同奖励信号和不同临床诊断训练的策略的明显模式，并展示了 DRL 在提供个性化和高效治疗建议方面的潜力。