Reinforcement Learning in Robotics: Examining Reinforcement Learning Algorithms for Training Robotic Agents to Perform Complex Tasks Autonomously
Keywords:
Reinforcement Learning, Robotics, Autonomous Agents, Deep Reinforcement Learning, Exploration Strategies, Meta-Learning, Robotic Manipulation, Robotic Locomotion, Navigation, Domain AdaptationAbstract
Reinforcement learning (RL) has emerged as a powerful paradigm for training robotic agents to perform complex tasks autonomously. In this paper, we provide an overview of RL algorithms and their applications in robotics. We discuss the challenges of applying RL to robotic systems, including the need for efficient exploration, robustness to environmental changes, and sample efficiency. We also review recent advancements in RL that have addressed these challenges, such as deep reinforcement learning and meta-learning. Furthermore, we present case studies of RL in robotics, highlighting successful applications in various domains, including manipulation, locomotion, and navigation. Finally, we discuss future research directions and challenges in RL for robotics, such as incorporating prior knowledge and domain adaptation.
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Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.
Raparthi, M., Dodda, S. B., & Maruthi, S. (2020). Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks. European Economic Letters (EEL), 10(1).
Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.
Raparthi, M., Dodda, S. B., & Maruthi, S. (2021). AI-Enhanced Imaging Analytics for Precision Diagnostics in Cardiovascular Health. European Economic Letters (EEL), 11(1).
Vyas, B. (2021). Ensuring Data Quality and Consistency in AI Systems through Kafka-Based Data Governance. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(1), 59-62.
Rajendran, R. M. (2021). Scalability and Distributed Computing in NET for Large-Scale AI Workloads. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(2), 136-141.
Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.
Vyas, B. (2022). Optimizing Data Ingestion and Streaming for AI Workloads: A Kafka-Centric Approach. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 1(1), 66-70.
Pargaonkar, S. (2021). Quality and Metrics in Software Quality Engineering. Journal of Science & Technology, 2(1), 62-69.
Vyas, B. Ethical Implications of Generative AI in Art and the Media. International Journal for Multidisciplinary Research (IJFMR), E-ISSN, 2582-2160.
Rajendran, R. M. (2022). Exploring the Impact of ML NET (http://ml. net/) on Healthcare Predictive Analytics and Patient Care. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 292-297.
Pargaonkar, S. (2021). The Crucial Role of Inspection in Software Quality Assurance. Journal of Science & Technology, 2(1), 70-77.
Pargaonkar, S. (2021). Unveiling the Future: Cybernetic Dynamics in Quality Assurance and Testing for Software Development. Journal of Science & Technology, 2(1), 78-84.
Pargaonkar, S. (2021). Unveiling the Challenges, A Comprehensive Review of Common Hurdles in Maintaining Software Quality. Journal of Science & Technology, 2(1), 85-94.
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