Intelligent task allocation between moving objects based on reinforcement learning algorithm

Abstract

This paper provides a review of task allocation in robot groups using reinforcement learning algorithms. Based on this review, it is concluded that there is a need to develop task allocation methods based on reinforcement learning. The variable number of robots and target points must be taken into account. The paper formulates a task allocation problem for a group of robots functioning in a discrete environment with obstacles. Two variants of Q-algorithm with deep neural network for task allocation are proposed. The first algorithm is a centralized one. It uses a single neural network that approximates the value of the actions of all robots in the group. The second algorithm is a distributed one. This algorithm on board each robot computes the selection values of each target point in the current state. The proposed algorithms are investigated by numerical modeling methods for an environment with a variable number of robots and target points.