Abstract: Robot path planning has always been a hot topic in robot motion control.The current path planning takes a lot of time to build the map,but the reinforcement learning based on continuous “trial and error” mechanism can realize the mapless navigation.Through the research and analysis of current various deep reinforcement learning algorithms,using low-dimensional radar data and a small amount of position information can follow a moving target and avoid collisions in indoor environments.The results show that DQN、Dueling Double DQN and DDPG algorithms based on priority sampling present strong generalization capabilities in different environment.

Key words: Path planning, Reinforcement learning, Target following