Hierarchical Apprenticeship Learning from Imperfect Demonstrations with Evolving Rewards
Summary: arXiv:2604.00258v1 Announce Type: cross
Abstract
While apprenticeship learning has shown promise for inducing effective pedagogical policies directly from student interactions in e-learning environments, most existing approaches rely on optimal or near-optimal expert demonstrations under a fixed reward. Real-world student interactions, however, are often inherently imperfect and evolving: students explore, make errors, revise strategies, and refine their goals as understanding develops.
In this work, we argue that imperfect student demonstrations are not noise to be discarded, but structured signals—provided their relative quality is ranked. We introduce HALIDE, Hierarchical Apprenticeship Learning from Imperfect Demonstrations with Evolving Rewards, which not only leverages sub-optimal student demonstrations but ranks them within a hierarchical learning framework.
Key Features of HALIDE
- Hierarchical Learning Framework: HALIDE models student behavior at multiple levels of abstraction, enabling inference of higher-level intent and strategy from suboptimal actions.
- Temporal Evolution of Rewards: The model explicitly captures the temporal evolution of student reward functions, allowing it to adapt to changing student needs and goals.
- Integration of Demonstration Quality: By integrating demonstration quality into hierarchical reward inference, HALIDE distinguishes between transient errors and meaningful progress toward higher-level learning goals.
Methodology
The HALIDE framework employs a sophisticated ranking mechanism for imperfect demonstrations. Rather than treating these demonstrations as mere noise, the system recognizes their potential as informative signals that can guide learning. This approach allows HALIDE to build a more nuanced understanding of student behavior, leading to better decision-making processes in pedagogical contexts.
Additionally, HALIDE’s multi-level abstraction enables the model to infer the underlying intent behind student actions, even when those actions deviate from optimal strategies. This capability is particularly relevant in real-world learning scenarios, where students often experiment and learn through trial and error.
Results and Implications
The results of our experiments indicate that HALIDE significantly outperforms traditional approaches that rely on optimal trajectories, fixed rewards, or unranked imperfect demonstrations. By accurately predicting student pedagogical decisions, HALIDE demonstrates the effectiveness of leveraging imperfect demonstrations for enhancing learning outcomes.
The implications of this research extend beyond e-learning environments. By understanding and integrating the complexities of real-world learning processes, HALIDE can inform the design of more adaptive and responsive educational technologies, ultimately leading to improved student engagement and success.
Conclusion
In summary, HALIDE represents a significant advancement in apprenticeship learning by embracing the imperfect nature of student demonstrations and evolving rewards. By recognizing the structured signals within these imperfections, HALIDE enhances the predictive capabilities of pedagogical models, paving the way for more effective and personalized learning experiences in various educational settings.