UPEGSim: An RL-Enabled Simulator for Unmanned Underwater Vehicles Dedicated in the Underwater Pursuit-Evasion Game

Abstract

Unmanned underwater vehicles (UUVs) have been widely used in various ocean applications, such as underwater exploration and data collection. And the underwater pursuit-evasion game (UPEG) is the key to efficient implementation of other tasks, holding significant research value. However, testing the UPEG task in real ocean environment is both costly and risky, and currently, UUV control algorithms that rely on specific environmental models struggle to complete the complicated UPEG task. To address above challenge, we propose UPEGSim, an UUV simulator specifically designed for the UPEG task. Built through Gazebo and robot operating system, UPEGSim provides a reinforcement learning (RL) environment to train UUVs for improving the intelligent performance in the UPEG task. Furthermore, we propose an efficient UPEG training framework (ETFDU), which includes multiagent decentralized training and execution techniques, scene transfer training methods, and offline RL techniques based on decision transformer, to facilitate efficient UUV training. Through training on the UPEG task in UPEGSim, we validate the effectiveness and feasibility of the proposed UPEGSim simulator and the ETFDU training framework.