Research on Autonomous Robots Navigation based on Reinforcement Learning

Understand

Reinforcement learning continuously optimizes decision-making based on real-time feedback reward signals through continuous interaction with the environment, demonstrating strong adaptive and self-learning capabilities.

• In recent years, it has become one of the key methods to achieve autonomous navigation of robots.
• In this work, an autonomous robot navigation method based on reinforcement learning is introduced.
• We use the Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models to optimize the path planning and decision-making process through the continuous interaction between the robot and the environment, and the reward signals with real-time feedback.