Reinforcement Learning Q-Learning Algorithm: Theory and Implementation

The Q-learning algorithm in reinforcement learning is a value-based iterative algorithm that learns and optimizes by continuously updating the value function for state-action pairs. In this algorithm, each action is associated with a Q-value representing its utility in a specific state. Through repeated iterations and updates of these Q-values using the Bellman equation (Q(s,a) ← Q(s,a) + α[r + γmaxQ(s',a') - Q(s,a)]), the algorithm reinforces the value of specific actions in particular states, ultimately achieving learning and optimization objectives. Key implementation components include: maintaining a Q-table for state-action value storage, implementing an epsilon-greedy policy for exploration-exploitation balance, and setting appropriate learning rates (α) and discount factors (γ). This approach enables agents to learn optimal policies without requiring a model of the environment. We hope this explanation, enhanced with algorithmic details, proves helpful for those implementing Q-learning in practical applications.