Applications for Reinforcement Learning in Robotics: A Comprehensive Review

Abstract

Reinforcement learning has become a promising and effective method to solve robots' complex control problems, enabling agents to learn optimal behaviors independently in a dynamic, uncertain, and usually high-dimensional environment. This paper comprehensively reviews the latest progress in applying reinforcement learning technology in robotics, paying special attention to key areas such as path planning, dynamic obstacle avoidance, multi-robot cooperation, and human-computer interaction. By critically analyzing various methods based on reinforcement learning, this paper emphasizes the key progress in improving robot autonomy, decision-making, and adaptability. It also discusses the challenges of deploying reinforcement learning in real-world robot applications, including sample efficiency, security, scalability, and generalization to new environments. In particular, Deep Reinforcement Learning (DRL) shows great potential in enhancing the robot's ability, especially in a complex, unstructured environment. However, challenges such as high computing costs and long training time still need to be addressed. Finally, the paper suggests that future research should focus on the hybrid RL method, improve learning efficiency, combine domain knowledge, and combine RL with other advanced technologies such as computer vision, multi-agent system, and real-time feedback mechanism, to further expand its potential application and increase its influence in the field of robotics.