Research on Multi-robot Path Planning Methods Based on Learning Classifier System with Gradient Descent Methods

This paper deals with the problem of multi-robot path planning based on learning classifier system in a dynamic narrow environment, where the workspace is cluttered with unpredictably moving objects. A Learning Classifier System is an accuracy-based machine learning system with gradient descent that combines reinforcement learning and rule discovery system. The genetic algorithm and the covering operator act as innovation discovery components which are responsible for discovering new better path planning rules. The reinforcement learning component is responsible for adjusting the fitness of rules in the system according to some reward obtained from the environment. The advantage of this approach is its accuracy-based representation, which can easily reduce learning space, improve online learning ability and robot robustness.