An Explanatory Example of Q-Learning Implementation

In this article, we present an explanatory example of Q-learning to help you better understand this reinforcement learning concept. The example is organized as a compressed archive containing two MATLAB script files (.m files). When you execute these files, they will generate observable output results demonstrating the Q-learning process. This example demonstrates the working mechanism of Q-learning and illustrates how it can be applied to real-world problems. We provide a detailed, step-by-step breakdown of the implementation process with comprehensive explanations for each stage. The code implements the core Q-learning algorithm through a Q-table update mechanism using the Bellman equation: Q(s,a) = Q(s,a) + α[r + γmaxQ(s',a') - Q(s,a)], where α represents the learning rate and γ the discount factor. Through this practical implementation, you will gain understanding of key Q-learning concepts including: - State-action value initialization and iteration - Reward function design - Exploration vs exploitation strategies (ε-greedy approach) - Convergence criteria and performance metrics The example provides hands-on experience with how Q-learning can be utilized in practical applications, featuring code segments that handle environment interactions, policy optimization, and result visualization through MATLAB's plotting capabilities.