Talk:Alpha Function in Q-Learning

I thought this article was good. To make the article flow a little easier, it might help to introduce your proposed alpha functions earlier. Additionally, you could give background on why you chose those specific alpha functions. I had a couple questions about your article like how many steps did you perform on the tiny game until you confirmed the alpha function did or did not converge? For the first alpha function, how do you know it will not converge in many steps?

It would also be helpful to see which alpha functions reach convergence first, this could be demonstrated on a graph.

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!