The article "Reinforcement Learning in Robotics: A Survey" by Jens Kober, J. Andrew Bagnell, and Jan Peters explores the application of reinforcement learning (RL) in robotics, highlighting its potential to design sophisticated behaviors. RL offers a framework for robots to learn optimal behaviors through trial-and-error interactions with their environment, guided by a scalar objective function. The authors discuss the challenges and successes in robot reinforcement learning, emphasizing the choice between model-based and model-free approaches, as well as value function-based and policy search methods. They analyze the complexity of the domain and the role of algorithms, representations, and prior knowledge in achieving successful outcomes. The paper provides a comprehensive overview of real robot reinforcement learning tasks, focusing on physical robots with high-dimensional, continuous states and actions, and addresses issues such as reward shaping, under-modeling, and the exploration-exploitation trade-off. The authors aim to bridge the gap between the robotics and machine learning communities, providing insights into the applicability of RL in robotics and highlighting open questions and future research directions.The article "Reinforcement Learning in Robotics: A Survey" by Jens Kober, J. Andrew Bagnell, and Jan Peters explores the application of reinforcement learning (RL) in robotics, highlighting its potential to design sophisticated behaviors. RL offers a framework for robots to learn optimal behaviors through trial-and-error interactions with their environment, guided by a scalar objective function. The authors discuss the challenges and successes in robot reinforcement learning, emphasizing the choice between model-based and model-free approaches, as well as value function-based and policy search methods. They analyze the complexity of the domain and the role of algorithms, representations, and prior knowledge in achieving successful outcomes. The paper provides a comprehensive overview of real robot reinforcement learning tasks, focusing on physical robots with high-dimensional, continuous states and actions, and addresses issues such as reward shaping, under-modeling, and the exploration-exploitation trade-off. The authors aim to bridge the gap between the robotics and machine learning communities, providing insights into the applicability of RL in robotics and highlighting open questions and future research directions.