2026 Vol.9 Jun.N03

• Title: Optimization of Deep Reinforcement Learning Pathways for AI-Empowered
• Name: Zhihui Zou1,2
• Company: (1.Changchun Polytechnic University,Changchun,130033,China; 2.College OF Humanities & Information Changchun University OF Technology,Changchun,130122,China)
• Abstract:

  Optimization schemes for “Two-Teacher, Three-Platform, Four-Dimensional” dynamic collaborative education typically rely on fixed rules to allocate educational pathways. However, due to the difficulty in balancing the interactive relationships among multiple stakeholders, these schemes often result in educational pathways that lack adaptability. To address this, we propose a deep reinforcement learning approach for optimizing AI-enabled “Two-Teacher, Three-Platform, Four-Dimensional” dynamic collaborative education.By integrating three core educational elements—the collaborative performance of dual-teacher teams, the coordinated operation of multiple platforms, and the implementation of four-dimensional education—we calculate real-time overall educational scenario state values and define the amplitude of dynamic fluctuations in the scenario based on the state differences between adjacent time periods. Combining the positive gains from the current overall educational state with the educational losses resulting from scenario fluctuations, we calculate the immediate benefits of educational control actions, thereby intuitively reflecting the actual effectiveness of educational adjustments within a single time period.Building on this, a benefit decay coefficient is introduced to factor in the projected cumulative benefits of subsequent time periods, thereby constructing a long-term benefit estimation model. Using cumulative benefits as the basis for optimization, the agent continuously iterates through trial and error to update the corresponding execution strategies for dual-teacher collaboration, the platform, and four-dimensional education, thereby selecting the collaborative education path that yields the optimal long-term benefits.In the experiment, the proposed method was tested for educational adaptability. The test comparison results clearly show that when the proposed method is used for deep reinforcement learning path optimization in dynamic collaborative education, the educational dynamic adaptability converges to 91.2%, demonstrating relatively ideal optimization results.

  
  

• Keyword: AI-enabled; dual-teacher classroom; collaborative education; deep reinforcement learning; path optimization;
• DOI: 10.12250/jpciams2026090602
• Citation form: Zhihui Zou.Optimization of Deep Reinforcement Learning Pathways for AI-Empowered[J]. Computer Informatization and Mechanical System,2026,Vol.9,pp.

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