# Deep Reinforcement Learning for Adaptive Learning Systems

arXiv: https://arxiv.org/abs/2004.08410 · HTML: https://mlsanity.com/paper/2004.08410/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing](https://mlsanity.com/paper/2108.07386/) (2021)
- [Survey of Computerized Adaptive Testing: A Machine Learning Perspective](https://mlsanity.com/paper/2404.00712/) (2024)
- [Amortised Design Optimization for Item Response Theory](https://mlsanity.com/paper/2307.09891/) (2023)
- [Simulated Human Learning in a Dynamic, Partially-Observed, Time-Series Environment](https://mlsanity.com/paper/2511.15032/) (2025)
- [AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems](https://mlsanity.com/paper/2605.21962/) (2026)

## Abstract (arXiv)

In this paper, we formulate the adaptive learning problem---the problem of how to find an individualized learning plan (called policy) that chooses the most appropriate learning materials based on learner's latent traits---faced in adaptive learning systems as a Markov decision process (MDP). We assume latent traits to be continuous with an unknown transition model. We apply a model-free deep reinforcement learning algorithm---the deep Q-learning algorithm---that can effectively find the optimal learning policy from data on learners' learning process without knowing the actual transition model of the learners' continuous latent traits. To efficiently utilize available data, we also develop a transition model estimator that emulates the learner's learning process using neural networks. The transition model estimator can be used in the deep Q-learning algorithm so that it can more efficiently discover the optimal learning policy for a learner. Numerical simulation studies verify that the proposed algorithm is very efficient in finding a good learning policy, especially with the aid of a transition model estimator, it can find the optimal learning policy after training using a small number of learners.