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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