A Reinforcement Learning Approach Based on Automatic Policy Amendment for Multi-AUV Task Allocation in Ocean Current

Author:

Ding Cheng,Zheng Zhi

Abstract

In this paper, the multiple autonomous underwater vehicles (AUVs) task allocation (TA) problem in ocean current environment based on a novel reinforcement learning approach is studied. First, the ocean current environment including direction and intensity is established and a reward function is designed, in which the AUVs are required to consider the ocean current, the task emergency and the energy constraints to find the optimal TA strategy. Then, an automatic policy amendment algorithm (APAA) is proposed to solve the drawback of slow convergence in reinforcement learning (RL). In APAA, the task sequences with higher team cumulative reward (TCR) are recorded to construct task sequence matrix (TSM). After that, the TCR, the subtask reward (SR) and the entropy are used to evaluate TSM to generate amendment probability, which adjusts the action distribution to increase the chances of choosing those more valuable actions. Finally, the simulation results are provided to verify the effectiveness of the proposed approach. The convergence performance of APAA is also better than DDQN, PER and PPO-Clip.

Funder

The National Natural Science Foundation of China; The Science Foundation of Fujian Normal University; The Natural Science Foundation of Fujian Province

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering

Reference38 articles.

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