Reinforcement Learning
Reinforcement Learning: A Case Study on Algorithmic Design and Performance Introduction Reinforcement learning (RL) is one of the most exciting and rapidly advancing areas of machine learning, sitting at the intersection of artificial intelligence, dynamic programming, game theory, and control theory. The core principle behind RL is learning from interaction —an agent learns to make decisions by interacting with its environment, receiving rewards or penalties based on the outcomes of its actions. Through this trial-and-error process, the agent refines its strategy to maximize cumulative rewards. Unlike supervised learning, where models rely on labeled datasets, RL agents must learn autonomously from feedback provided by the environment, making it ideal for solving complex decision-making problems. In this blog, we will explore RL through the lens of one of its foundational algorithms— Q-Learning , and its more advanced extension, Deep Q Networks (DQN) . These algorithms illustrat...