# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 2 Background  
### Reinforcement Learning (RL) in Robotics  
RL enables robots to enhance their skills by learning from their environment and gaining experience. They can adapt flexibly to diverse and unpredictable situations, continuously improving their behavior. Rather than following predefined instructions, agents adjust their actions based on the experience gained, enabling them to navigate in complex, dynamic, and unpredictable environments. \begin{table}
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**Title** & **Publisher** & **Description** \\ \hline Reinforcement Learning: A Survey [27] & JAIR 1996 & Comprehensive overview of RL and summary of the historical basis and a broad selection of current work. \\ \hline Convergence of Machine Learning and Robotics Communication in Collaborative Assembly Mobility, Connectivity, and Future Perspectives [6] & Springer 2019 & Explores the integration of ML in robots to enhance communication. \\ \hline Survey of Model-Based Reinforcement Learning: Applications on Robotics [28] & JINT 2017 & Categorizes model-based robotics techniques and discusses their applicability to new applications. \\ \hline A Survey of Robots in Healthcare [5] & MDPI 2021 & Categorizes 38 robotic platforms in healthcare settings, providing insights into their applications. \\ \hline Reinforcement learning for robot research: A comprehensive review and open issues [9] & SAGE 2021 & Comprehensive review of RL applications in robotics, covering algorithms, challenges, and research directions. \\ \hline Robot Operating System: 2 Design, architecture, and uses in the wild [29] & Science 2022 & Outlines the design architecture and improvement of ROS versions and highlights their real-world applications. \\ \hline Artificial intelligence, machine learning and deep learning in advanced robotics, a review [8] & KeAi 2023 & Reviews the applications of AI, ML, and DL techniques in advanced robotics and discusses the impact and future possibilities. \\ \hline A Scoping review of Deep Reinforcement Learning methods in Visual Navigation [10] & IEEE 2023 & Review of DRL methods used in visual navigation tasks, examining their strengths, weaknesses, and improvements. \\ \hline A Survey of Machine Learning Approaches for Mobile Robot Control [7] & MDPI 2024 & Comprehensive review of various ML algorithms applied to diverse mobile robot tasks, including navigation, obstacle avoidance, and more. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of previous survey papers. RL is a Deep Learning (DL) system in which an agent, such as a robot in our case, interacts with the environment to achieve a goal by learning from the feedback received. The agent observes the state of the environment, selects an action based on a policy, responds to a reward, and adjusts its policy to maximize rewards, as shown in Figure 1.  
This is a repeated process which allows the agent (robot) to learn the best action to choose for the next state through trial and error, which leads to improving the agent performance over time. RL incorporates several methodologies, including collecting data, logical decision making, and oversight correction, to improve learning techniques and examine the impact of input parameters on possible actions. Consistently improving the learning process and adjusting to the sensory inputs allows agents (robots) to gradually enhance their interaction abilities within their environment. RL enhances decision making by learning through repeated interactions with environments, with each action resulting in a reward. This is further used to learn a policy that guides the agent (robot) toward maximizing cumulative rewards. Performance improvements based on RL will help robots learn optimal behavior(s), thereby improving efficiency and effectiveness in activities such as control, navigation, and manipulation. [PERSON]'s contributions to dynamic programming laid the groundwork for the development of RL algorithms and revolutionized decision making in uncertain situations [30; 31].