Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education

• URL: http://arxiv.org/abs/2509.05689v2
• Date: Sat, 27 Sep 2025 10:31:34 GMT
• Title: Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education
• Authors: Muhammad Ahmed Atif, Mohammad Shahid Shaikh,
• Abstract summary: The proposed approach integrates traditional lectures with interactive lab-based learning.<n>The framework engages students through real-time coding exercises using simulated environments such as OpenAI Gymnasium.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This innovative practice category paper presents an innovative framework for teaching Reinforcement Learning (RL) at the undergraduate level. Recognizing the challenges posed by the complex theoretical foundations of the subject and the need for hands-on algorithmic practice, the proposed approach integrates traditional lectures with interactive lab-based learning. Drawing inspiration from effective pedagogical practices in computer science and engineering, the framework engages students through real-time coding exercises using simulated environments such as OpenAI Gymnasium. The effectiveness of this approach is evaluated through student surveys, instructor feedback, and course performance metrics, demonstrating improvements in understanding, debugging, parameter tuning, and model evaluation. Ultimately, the study provides valuable insight into making Reinforcement Learning more accessible and engaging, thereby equipping students with essential problem-solving skills for real-world applications in Artificial Intelligence.

Related papers

• Large Language Models in Architecture Studio: A Framework for Learning Outcomes [0.0]
  The study explores the role of large language models (LLMs) in the context of the architectural design studio.<n>The main challenges include managing student autonomy, tensions in peer feedback, and the difficulty of balancing the transmission of technical knowledge with the stimulation of creativity in teaching.
  arXiv  Detail & Related papers  (2025-10-08T02:51:22Z)
• Real-time classification of EEG signals using Machine Learning deployment [0.0]
  This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic.<n>A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic.
  arXiv  Detail & Related papers  (2024-12-27T08:14:28Z)
• Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning [0.0]
  This study explores the performance, robustness, and limitations of imitation learning compared to traditional reinforcement learning methods. The insights gained from this study contribute to the advancement of human-centered artificial intelligence.
  arXiv  Detail & Related papers  (2024-10-28T18:07:44Z)
• Revolutionising Role-Playing Games with ChatGPT [0.0]
  The aim of the study was to analyse the impact of AI-based simulations on students' learning experience. Based on Vygotsky's sociocultural theory, ChatGPT was used to give students a deeper understanding of strategic decision-making processes.
  arXiv  Detail & Related papers  (2024-07-02T08:21:40Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Towards a General Framework for Continual Learning with Pre-training [55.88910947643436]
  We present a general framework for continual learning of sequentially arrived tasks with the use of pre-training. We decompose its objective into three hierarchical components, including within-task prediction, task-identity inference, and task-adaptive prediction. We propose an innovative approach to explicitly optimize these components with parameter-efficient fine-tuning (PEFT) techniques and representation statistics.
  arXiv  Detail & Related papers  (2023-10-21T02:03:38Z)
• A Game-Based Learning Application to Help Learners to Practice Mathematical Patterns and Structures [0.0]
  The purpose of this study is to develop a game-based mobile application to help learners practice mathematical patterns and structures. An instrument based on the Octalysis framework was developed as an evaluation tool for the study.
  arXiv  Detail & Related papers  (2023-06-22T13:15:12Z)
• RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions [10.34673089426247]
  We propose a framework for optimizing teaching strategies by constructing a virtual model of the student. Our results can serve as a buffer between theoretical instructional optimization and practical applications in e-learning systems.
  arXiv  Detail & Related papers  (2021-07-31T15:42:03Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)
• Importance Weighted Policy Learning and Adaptation [89.46467771037054]
  We study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning. The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior. Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
  arXiv  Detail & Related papers  (2020-09-10T14:16:58Z)
• A Competence-aware Curriculum for Visual Concepts Learning via Question Answering [95.35905804211698]
  We propose a competence-aware curriculum for visual concept learning in a question-answering manner. We design a neural-symbolic concept learner for learning the visual concepts and a multi-dimensional Item Response Theory (mIRT) model for guiding the learning process. Experimental results on CLEVR show that with a competence-aware curriculum, the proposed method achieves state-of-the-art performances.
  arXiv  Detail & Related papers  (2020-07-03T05:08:09Z)
• Dual Policy Distillation [58.43610940026261]
  Policy distillation, which transfers a teacher policy to a student policy, has achieved great success in challenging tasks of deep reinforcement learning. In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment. The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2020-06-07T06:49:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.