Reinforcement Learning for Robotics

Through trial-and-error interactions with their surroundings, robots can learn optimal behaviours thanks tothe potent robotics technique known as reinforcement learning (RL). Robots use reinforcement learning (RL)to improve their actions based on feedback in the form of rewards or penalties, rather than being explicitlyprogrammed. This is particularly helpful in dynamic or complex environments where conventional controltechniques are ineffective. RL has been effectively used in fields like legged locomotion, autonomousnavigation, and robotic manipulation. High sample complexity, safety in real-world learning, and the sim-toreal transfer—the gap between simulation and real.