Assignment Feedback

Your thorough exploration of Q-learning and the in-depth analysis of various α (alpha) functions greatly contributes to understanding reinforcement learning methods. The incorporation of theoretical aspects, including the Robbins-Monro Conditions, is commendable and provides a solid theoretical framework for assessing the convergence of Q-learning agents with specific α functions. However, to enhance the article further, it might be beneficial to provide more context on the practical significance of the Robbins-Monro Conditions. Explain how meeting these conditions ensures convergence in the context of Q-learning and reinforce why these conditions are crucial for the reliability of the algorithm.

Additionally, while you've successfully demonstrated the convergence of two α functions in both theory and practice, the article could benefit from a deeper discussion on the implications of these results. For instance, elaborate on why certain α functions, like α(k) = 1/k, fail to converge in practice and discuss potential implications for real-world applications. Providing insights into the practical limitations or challenges associated with specific α functions would add depth to your findings.

Lastly, consider expanding on the practical implications of the Q-learning hyperparameters you chose, such as the discount factor (γ), initial Q-values, and exploration strategy. Discuss how variations in these hyperparameters might impact the Q-learning agent's performance in different scenarios, providing readers with a more nuanced understanding of the algorithm's sensitivity to these choices. This additional information will offer readers a more comprehensive view of the factors influencing the practical application of Q-learning. Overall, your article is insightful, and these suggested enhancements can further enrich the reader's experience. Great job!