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

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.