In the realm of artificial intelligence and machine learning, a revolutionary algorithm has been making waves – Q-Learning. Originally conceived as an incremental algorithm for optimal decision-making, Q-learning has evolved into a powerhouse in reinforcement learning. This piece delves into an exploration to unravel the intricacies of single-agent and multi-agent Q-learning algorithms, exploring their applications
