Kişiselleştirilmiş Psikolojik Müdahale Sistemi, Transformer ve Derin Pekiştirmeli Öğrenme İşbirliği ile Optimize Ediliyor

Özet (abstract)

Long-term digital psychological intervention faces issues such as delayed state feedback, observation absence, and scattered individual responses. Static input is difficult to retain cross-period dependencies, and a unified strategy will also widen user differences and cause fluctuations in continuous actions. To address these problems, a personalized intervention system that combines Transformer temporal modeling and proximal policy optimization is constructed. Structured psychological state representation organizes continuous behaviors and subjective feedback into an updateable sequence. The Transformer encoder extracts long-term dependencies and local mutations, the individual embedding absorbs user response deviations, and the Kullback–Leibler (KL) constraint inhibits adjacent strategy jumps. The experiment uses StudentLife data and accompanying simulations, semi-synthetic data. The cumulative reward of the complete model reaches 634.7 ± 21.8, with a 95% confidence interval of 619.11–650.29. The rule strategy, Proximal Policy Optimization (PPO) immediate state model, and the configuration without individual embedding are 412.6 ± 38.4, 528.3 ± 42.7, and 561.9 ± 35.2, respectively. The differences among the three groups, after Holm correction, all reach significant levels ( p < 0.001). The complete model enters the stable range after 1620 rounds, and the variance of the reward after convergence drops to 19.4. The rate of change in emotional intensity, the improvement value of state stability, and the cumulative variance of individual rewards is 0.091, 0.052, and 28.4, respectively. The ablation results show that temporal encoding mainly affects long-term benefits and convergence stability, and individual embedding focuses on reducing the dispersion among users. The existing evidence supports strategy optimization in non-clinical environments, but does not constitute a psychological mechanism explanation, causal effect proof, or clinical effectiveness conclusion.

Yazarların özeti; kaynağından alınmıştır. Discover Artificial Intelligence, 2026 · DOI ↗