A Robot Autonomous Navigation System Based on Deep Reinforcement Learning

This paper provides an in-depth review of advancements in robotic motion control systems through the application of Deep Reinforcement Learning (DRL). The study highlights the growing complexity of robotic tasks. It emphasizes the need for adaptive control strategies in dynamic, uncertain environments where traditional methods fall short. By categorizing DRL algorithms into value-based approaches, such as Deep Q-Networks (DQN), and policy gradient methods, like Proximal Policy Optimization (PPO), the paper offers a comparative analysis of their applications in robotic systems. Key areas explored include autonomous navigation, object manipulation, and human-robot interaction. "The authors present specific simulation results, including performance metrics and comparative data, to demonstrate DRL's superior capabilities in real-time robotic control." Challenges in transferring DRL from simulation to real-world applications, such as sensor noise and computational constraints, are also discussed. The paper concludes by suggesting future research directions, particularly the integration of DRL with other machine learning techniques to enhance robot capabilities in multi-agent systems.