Reinforcement learning is a subfield of machine learning where an agent learns to make decisions by interacting with its environment. The article provides an overview of the field, highlighting key concepts such as Q-learning and SARSA algorithms. These algorithms enable agents to learn from trial and error, adjusting their actions based on rewards or penalties. The author also discusses exploration-exploitation trade-offs and other challenges in reinforcement learning. --- Why it matters: This matters because reinforcement learning has applications in areas like robotics, game playing, and autonomous vehicles, where agents must make decisions in real-world environments. Source: https://lilianweng.github.io/posts/2018-02-19-rl-overview/