Path Planning for Mobile Robots Using Transfer Reinforcement Learning

The path planning of mobile robots helps robots to perceive environment using the information obtained from sensors and plan a route to reach the target. With the increasing difficulty of task, the environment the mobile robots face becomes more and more complex. Traditional path planning methods can no longer meet the requirements of mobile robot navigation in complex environment. Deep reinforcement learning (DRL) is introduced into robot navigation However, it may be time-consuming to train DRL model when the environment is very complex and the existing environment may differ from the unknown environment. In order to handle the robot navigation in heterogeneous environment, this paper utilizes deep transfer reinforcement learning (DTRL) for mobile robot path planning. Compared with DRL, DTRL does not require the distribution of the existing environment is the same as that of the unknown environment. Additionally, DTRL can transfer the knowledge of existing model to new scenario to reduce the training time. The simulations show that DTRL can reach higher success rate than DRL for heterogeneous environment robot navigation. By using local policy, it costs less time to train DTRL than DRL for a complex environment and DTRL can consume less navigation time.