Reinforcement Learning in Robotics

Reinforcement Learning (RL) in robotics is a machine learning paradigm where robots learn to make decisions by interacting with their environment. In this approach, robots receive feedback in the form of rewards or penalties based on their actions, enabling them to learn optimal behaviors over time. Key characteristics of RL in robotics include the use of trial-and-error learning, the ability to handle complex, dynamic environments, and the focus on maximizing cumulative rewards. Common use cases include robotic navigation, manipulation tasks, and autonomous systems, where robots continuously improve their performance through experience and adapt to new challenges.