🤖 Reinforcement Learning (RL) in Robotics is a cutting-edge field where robots learn optimal behaviors through trial-and-error interactions with their environment — rather than being explicitly programmed for every task. RL empowers robots to adapt, improve, and even discover strategies in complex, dynamic environments. 🎯 What Is Reinforcement Learning? Reinforcement Learning is a type of machine learning where an agent (robot) learns to maximize cumulative reward by: Taking actions Observing results (state + reward) Learning from feedback It's modeled as a Markov Decision Process (MDP) : State (s): Robot’s current situation Action (a): Possible movements or commands Reward (r): Feedback (positive or negative) Policy (π): Strategy mapping states to actions Value function (V): Expected return from a state 🦾 Why Use RL in Robotics? 🤖 Autonomous Skill Learning – Robots can learn tasks without needing hand-coded control logic 🔄 C...