Reproducibility

Reinforcement Learning in Robotics is a machine learning technique where robots learn to perform tasks by interacting with their environment, receiving feedback through rewards or penalties. This trial-and-error process helps robots develop optimal behaviors without explicit programming for every situation, enabling adaptable, autonomous operation in complex, dynamic environments such as navigation, manipulation, and inspection tasks.