mlsanityData to 7 Oct 2026

Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning

arXiv 2104.10258 ↗First posted 20 Apr 2021cs.LGcited by 3

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

High dropout rates in tertiary education expose a lack of efficiency that causes frustration of expectations and financial waste. Predicting students at risk is not enough to avoid student dropout. Usually, an appropriate aid action must be discovered and applied in the proper time for each student. To tackle this sequential decision-making problem, we propose a decision support method to the selection of aid actions for students using offline reinforcement learning to support decision-makers effectively avoid student dropout. Additionally, a discretization of student's state space applying two different clustering methods is evaluated. Our experiments using logged data of real students shows, through off-policy evaluation, that the method should achieve roughly 1.0 to 1.5 times as much cumulative reward as the logged policy. So, it is feasible to help decision-makers apply appropriate aid actions and, possibly, reduce student dropout.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 19 of its 42 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

19 of 42 references.

Closest ancestors · 6

Same direction, newest first

  1. 2020Hyperparameter Selection for Offline Reinforcement Learning17 Jul
  2. 2020RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning24 Jun
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems4 May
  4. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learning15 Apr
  5. 2019Behavior Regularized Offline Reinforcement Learning26 Nov
  6. 2019Benchmarking Batch Deep Reinforcement Learning Algorithms3 Oct

Other references · 13

  1. 2015Trust Region Policy Optimizationcited by 2,503
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  4. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
  5. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  6. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  7. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 647
  8. 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 285
  9. 2019Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialogcited by 283
  10. 2018Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guaranteescited by 138
  11. 2020Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimizationcited by 120
  12. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
1 more
  1. 2018Horizon: Facebook's Open Source Applied Reinforcement Learning Platformcited by 48

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.

  1. 2022How to Leverage Unlabeled Data in Offline Reinforcement Learningcited by 61
  2. 2024PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performercited by 6
  3. 2025Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learningcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025A Graphical Approach to State Variable Selection in Off-policy Learning
  2. 2021Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
  3. 2022Offline Policy Evaluation and Optimization under Confounding
  4. 2026On-Policy Distillation Meets Off-Policy GRPO: Training Compact Instruction-Following Rerankers
  5. 2021Representation Matters: Offline Pretraining for Sequential Decision Making
  6. 2021Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning
  7. 2022On the Role of Discount Factor in Offline Reinforcement Learning
  8. 2024Augmenting Offline RL with Unlabeled Data