The Genesis of Learning from Interaction In the vast and intricate world of artificial intelligence, the concept of learning through interaction stands as a cornerstone, paving the way for systems that not only understand but adapt. This foundational premise is what we explore under the umbrella of reinforcement learning (RL). At its core, RL is a paradigm where agents learn to make decisions by interacting with their environment. This interaction is governed by the principle of trial and error, where actions lead to rewards or penalties, guiding the agent towards optimal behavior. The Pillars of Reinforcement