A Unified Batch Hierarchical Reinforcement Learning Framework For Pedagogical Policy Induction With Deep Bisimulation Metrics

Intelligent Tutoring Systems (ITSs) leverage AI to adapt to individual students, and employ pedagogical policies to decide what instructional action to take next. A number of researchers applied Reinforcement Learning (RL) and Deep RL (DRL) to induce effective pedagogical policies. Most prior work, however, has been developed independently for a specific ITS and cannot directly be applied to another. In this work, we propose a Multi-Task Learning framework that combines Deep BIsimulation Metrics and DRL, named MTL-BIM, to induce a unified pedagogical policy for two different ITSs across different domains: logic and probability. Based on empirical classroom results, our unified RL policy performed significantly better than the expert-crafted policies and independently induced DQN policies on both ITSs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.