Path planning Q-learning initial method of mobile robot

Abstract

The invention discloses a reinforcing learning initial method of a mobile robot based on an artificial potential field and relates to a path planning Q-learning initial method of the mobile robot. The working environment of the robot is virtualized to an artificial potential field. The potential values of all the states are confirmed by utilizing priori knowledge, so that the potential value of an obstacle area is zero, and a target point has the biggest potential value of the whole field; and at the moment, the potential value of each state of the artificial potential field stands for the biggest cumulative return obtained by following the best strategy of the corresponding state. Then a Q initial value is defined to the sum of the instant return of the current state and the maximum equivalent cumulative return of the following state. Known environmental information is mapped to a Q function initial value by the artificial potential field so as to integrate the priori knowledge into a learning system of the robot, so that the learning ability of the robot is improved in the reinforcing learning initial stage. Compared with the traditional Q-learning algorithm, the reinforcing learning initial method can efficiently improve the learning efficiency in the initial stage and speed up the algorithm convergence speed, and the algorithm convergence process is more stable.

Description

technical field [0001] The invention belongs to the technical field of machine learning, in particular to a Q-learning initialization method for path planning of a mobile robot. Background technique [0002] With the continuous expansion of the application field of robots, the tasks faced by robots are becoming more and more complex. Although researchers can pre-program the repetitive behaviors that robots may perform in many cases, the behavior design changes to achieve the overall desired behavior It is becoming more and more difficult for designers to make reasonable predictions about all the behaviors of robots in advance. Therefore, an autonomous robot capable of perceiving the environment must be able to learn new behaviors online by interacting with the environment, so that the robot can choose the optimal action to achieve the goal according to a specific task. [0003] Reinforcement learning uses a trial-and-error method similar to human thinking to discover the op...

AI Technical Summary

Problems solved by technology

The fuzzy rules established by this method are all artificially set according to the environmental information, which often cannot objectively reflect the environmental state of the robot, causing the algorithm to be unstable.

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