# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 1 Introduction  
ROS and RL are becoming quintessential in the robotics development field due to the ROS's adaptability and RL advancements in robot learning and decision making. The ROS offers tools and libraries for simulating and developing advanced robotic applications. The ROS enables real-time control and seamless integration for complex systems by facilitating communication among robotic components. In addition, RL is a rapidly evolving field that focuses on developing algorithms and improving the learning of optimal behaviors through trial-and-error scenarios. This process enhances robots' ability to autonomously adapt to real-life dynamics environments and tasks without relying on preprogrammed automation.  
Both ROS and RL have greatly influenced robotics by addressing various hardware and computational challenges. Improving hardware limitations by accurately aligningsensors with boundary conditions is one of the many problems that robotics have to deal with. These challenges affect the control and real-time processing of sensory data in dynamic and noisy environments, underscoring the reliance on sensory data for robotic navigation [1].  
Advances in robotic components, such as actuators and sensors, will enhance robot design and enhance interaction with their environments [2]. The modularity of ROS helps to deploy intelligent robot systems in various environments with minimal adjustments [3; 4], thereby providing a consistent user experience. Moreover, processing sensory data using the ROS as a middleware is crucial for providing secure task execution [5].  
Recent advances in Machine Learning (ML), specifically RL techniques, have greatly improved the functionality of the robot, leading to improved decision making and overall robot performance [6]. RL is now being widely used in robotics to achieve various objectives. These advanced robotic systems can effectively collect information from their environments and autonomously adjust by analyzing noisy sensory data and making predictions about their environment [7; 8; 9; 10].  
Although other works have explored the use of RL in robotics without using the ROS as a middleware, such as [11; 12; 13], the absence of the ROS affects the development of scalable, reusable, and modular robotic systems in dynamic settings [14; 15]. The lack of a unified middleware can lead to fragmented systems with poor interoperability, requiring the redevelopment of common functionalities such as message passing between robots [16; 17]. The incorporation of the ROS and RL has several advantages, such as superior decision making [9], improved perception [18], enhanced automation [19], and improved reliability of robotic operation [20]. The integration of the ROS with RL leverages the ROS's capabilities to create realistic and dynamic training environments using physics engine simulators such as Gazebo/V-Rep for Sim-to-Real deployments [21; 22].  
One such real-world application benefiting from the integration of the ROS and RL is the ABB Industrial Arm--demonstrating learned behavior for improved route planning [23]. This integration is essential for industrial autonomy, enabling robots to adapt and learn in dynamic contexts, thus enhancing productivity, minimizing mistakes, and facilitating the development of more autonomous robotic systems. Moreover, RL can automate operations that often require human intervention, resulting in increased efficiency and reduced costs. Ultimately, RL algorithms using ROS middleware will enhance a robot's real-time understanding of its surroundings, promoting reliable decision making and avoiding hazardous circumstances.