From RL Theory Paralysis to Algorithm Selection: A Practical Framework for Robotics

After weeks of staring at policy gradient equations, Bellman optimality conditions, and exploration-exploitation tradeoffs, you’re no closer to knowing which RL algorithm to use for your robotic arm. The textbooks cover the theory beautifully, but when you sit down to pick an algorithm for your actual robot, that theoretical knowledge feels distant and abstract. You’re... Continue Reading →

RL Fundamentals: Bandits & GridWorld Guide

Why Reinforcement Learning Feels Different (And Why That’s Good) If you’ve worked with supervised learning, you’re used to a straightforward paradigm: show the model labeled examples, and it learns to predict labels for new data. Unsupervised learning asks the model to find patterns in unlabeled data. Reinforcement Learning (RL) flips the script entirely. In RL,... Continue Reading →

Create a website or blog at WordPress.com

Up ↑